Patent Grant
8451929
The present invention relates multiple-output devices, and more particularly to optimizing signals associated with the multiple-output devices.
The field of wireless communication networks has challenged implementers with continuously discovered synergies, both positive and negative. The sea of signaling has long grown from scattered and isolated sparks of Morse code to the modern-day roar of intermingling transmissions. The simplicity of the directional link (Point to Point) was replaced by the broadcast (Point to Multipoint) and is being replaced by the mesh (Multipoint to Multipoint) and even the relaying, multi-hop, interactive mesh; also, the continuous-transmission format is being replaced by short and varying packets. The complexities of variations in real world conditions—constantly changing topography, overlapping wave signals, and unpredictable and intermittent faults or blockages—all challenge the existing methods and systems. Further description of some of these problems can be found in the parent application's text and will not be repeated here. This invention is concerned with the problems described below.
The primary problem solved through the invention is communication of short, intermittent packets, in particular Voice over IP (VoIP) communication signals, over communication networks subject to significant time-and-frequency co-incident (“co-channel”) interference from other network users and external emissions. A secondary problem solved through the invention is efficient physical routing of packets over multiple network nodes, e.g., using multihop relay techniques, in order to improve rate, robustness (immunity to interference and information warfare measures), reliability, and availability of communications between Source and Destination nodes in the network. A tertiary problem solved through the invention is means for rapid configuration and scalability of transceiver capabilities in highly dynamic environments where the density and severity of interferers, numbers and capabilities/requirements of communication nodes, and nature of channel propagation may change rapidly and dynamically between communication opportunities and/or over the course of a single communication opportunity.
The invention provides the following capabilities and advantages:
The invention is depicted in the Figures below.
Resolving many of the prior art's weaknesses by enabling true, opportunistic, MIMO networks is one of the principle objects of the invention. Creating a truly adaptive, flexible, multi-protocol wireless electromagnetic communications network is a second of the principle objects of the invention. A third is to simultaneously resolve the interplay between transmission power and network capacity by considering and using the interplay between one local node's transmissions as a signal and other nodes' reception of the same as either a signal (if the receiving node is an intended target) or as environmental noise (if the receiving node is an unintended target).
A secondary objective under this third objective is to provide methods which improve signal quality received by a targeted recipient node while simultaneously reducing interference energy received by other untargeted recipient nodes, so as to enable improved capacity amongst existing nodes, adding more nodes, increasing coverage area, and improving communications quality, or any sub-combination thereof.
Another secondary objective under this third objective is to provide an adaptive method which accounts for multipath interaction amongst the nodes and network, and minimizes unwanted effects while maximizing potential useful effects thereof.
Another secondary object under this is to provide improved load balancing amongst nodes and communication paths or links within the network with a minimum of overall control.
Another secondary object under this is to enable improved access to new nodes to the network.
Another secondary object under this is to enable multiple, competing, yet cooperating sub-networks that are mutually and automatically adaptive and responsive.
A second of the principal objects of the invention is to simultaneously resolve the interplay between local optimization, which demands detailed consideration of the immediate environmental details that affect each link between that node and others over which communications are flowing, and global optimization, which demands a minimum of control information be exchanged across the network and amongst the nodes in lieu of otherwise usable signal capacity.
A secondary object under this is to use the reception of signal information from other nodes, both those targeting the recipient and those not targeting the recipient, to enhance both reception and transmission quality to and from the receiving node while minimizing the explicit and separate feedback signals that must be exchanged amongst the nodes and network.
Another secondary object under this is to provide methods for optimization that can use or be independent of antenna array geometry, array calibration, or explicit feedback control signals from other nodes, whether the same are continuous, regular, or reactive to environmental changes affecting the link between the receiving node and the other nodes.
A third of the principal objects of the invention is to maximize the communications capacity and minimize the power usage both locally and globally across the network for any given set of hardware, software, and protocols.
A secondary object under this is to provide higher content throughput in underloaded networks, thereby providing faster perceived access or usage.
A secondary object under this is to provide higher reliability for any given hardware and software implementation.
A fourth of the principal objects of the invention is to provide a method for network optimization that can be extended to mixed networks, whether such mixing is amongst wireless and fixed links, or amongst electromagnetic spectra, or amongst types of nodes (BSs, dumb terminals, single- or limited-purpose appliances, or human-interactive input/output), and across both access schema and communications protocols with a minimum of particularization.
A fifth of the principal objects of the invention is to provide relatively simple and powerful methods for approximation which enable improvement that rapidly converges to the best solution for any optimization.
A secondary object under this is to provide a computationally efficient mechanization for cross-correlation operations that takes maximal advantage of multiport signals on particular single channels.
A sixth of the principle objects of the invention is to maximize the use of local information and minimize the use of global information that is required for approximation and approach to the best solution for any optimization.
The multiple-input, multiple-output (MIMO) network approach summarized here can incorporate as lesser, special cases, point-to-point links, point-to-multipoint networks, and disjoint (e.g., cellular) point-to-point links and point-to-multipoint networks. It can also be applied to spatial, temporal, or frequency-based access schemes (SDMA, TDMA, FDMA) employing combinations of spectral, temporal, spatial or polarization diversity, and to fixed, mixed, and mobile communications, as its focus is on the network in context rather than on the signal differentiation methodology, access determinations, or basing structure. One key to this approach is employment of spatially (or more generally diversity) adaptive transmit and receive processing to substantively reduce interference in general multipoint links, thereby optimizing capacity and/or other measures of network quality in multiply connected networks. A second key to this approach is the minimization of secondary consequences of signaling, and a second is using internalized feedback, so that the signaling process itself conveys information crucial to the optimization. Rapid, dynamic, adaptation reactive to the changing environment and communications within and surrounding each node and the entire network is used to promote both local and global efficiency. Unlike Varanesi, the feedback is neither limited to BSs only, nor effectively independent of the continual, real-world, signal and network environmental adaptation.
Instead of avoiding diversity, or fighting diversity, the present embodiment of the invention exploits and makes use of spectral, temporal, polarization, and spatial diversity available at each node, as well as and route (location based) diversity provided over the network of nodes. The network of nodes uses MIMO-capable nodes, that is, nodes with multiple antennae, multitone transceivers and preferentially reciprocal uplinks and downlinks (
The concept of a ‘diversity channel’ is introduced to permit a distinction to be made between “channels” that data is redundantly distributed across during receive (or transmit) operations, from “channels” that data is transported over (e.g., frequency channels or time slots). Data is redundantly transported over diversity channels, i.e., the same data is transported on each diversity channel with weighting determined by the methods described in detail below, while independent data is generally transported over the second flavor of channel. Effectively exploiting available diversity dimensions, the present embodiment of the invention can maximize its ability to attain Rank 2 capacities, since multiple redundant transmissions can be made over the plurality resources, whether that plurality comes from different frequencies, multipaths, time slots, spatially separable antennae, or polarizing antennae. This is distinct from prior art approaches which required multiple redundant transmissions over a plurality of frequencies to attain Rank 2 capacities. Because the signal flow between nodes is not limited to a particular dimension of substantive differentiation the preferred embodiment of the network can at every point in time, and for every node in the network, exploit any and all diversity opportunities practicable and attainable for the network's communication channels.
The signal flow between the multiplicity of nodes in the network comprises a multiplicity of information channels, emanating from and being received by a set of antennae at each node (
The preferred embodiment performs complex digital signal manipulation that includes a linear combining and linear distribution of the transmit and receive weights, the generation of piloting signals containing origination and destination node information, as well as interference-avoiding pseudorandom delay timing (
The network is designed such that a subset of its nodes are MIMO-capable nodes, and that each such node can simultaneously communicate with up to as many nodes in its field of view as it has antennae. The network is further designed such that it comprises two or more proper subsets, each proper subset containing members who cannot communicate directly with other members of the same proper susbset. So if the network contained only two proper subsets, First and Second, the members of First could transmit only to, and receive only from, the members of Second, and the members of Second could transmit only to, and receive only from, the members of First. Independent information is then transmitted from every member of First and is independently processed by each member of Second. (See
A non-MIMO-capable node may belong to any subset containing at least one MIMO-capable node that has at least one antenna available to that non-MIMO-capable node that has the non-MIMO-capable node in its field of view.
Diversity channels, rather than antennae, limits the number of non-set members a MIMO-capable node may communicate with simultaneously, that is, the number with which it may hold time/frequency coincident communications. Also, this limiting number is a function of number of users attempting to communicate over the same diversity channels—users on different time-frequency channels do not affect this limit. Thus a node with 128 time frequency channels (8 TDMA time slots×16 FDMA frequency channels) and a 4 antennas (4 diversity channels per time-frequency channel) can support up to 4×128=512 links, to as many users. If the internode channel response to a given user has rank 1 (e.g., if antennas are on same polarization and multipath is absent), then only a single link can be established to that user on each time-frequency channel, e.g., 128 separate links (one on each time-frequency channel) in the example given above. Higher internode channel rank allows more channels to be established; for example if nodes are polarization diverse, then the internode channel response has rank 2 and 256 channels (2 per time-frequency channel) can be established. The MIMO channel response equation determines power on each channel—depending on needs of network and pathloss to user, some or most of channels nominally available may be turned off to optimize the overall network capacity.
A significant element is that the diversity channel distribution need not be equal; one recipient node may have half the channels, if the traffic density requires it, while the transmitting node may divide its remaining channels evenly amongst the remaining nodes. Therefore the more users that have rank 2 or better capacity, the greater the available channels for those who have only capacity 1. This supports incremental optimization as improvement for the network is not dependent upon global replacement of every lesser-capacity node, but results from any local replacement.
The preferred embodiment details the means for handling the two alternative cases where the interference is, or is not, spatially white in both link directions, the means for handling interference that is temporally white over the signal passband. Preferentially, each link in the network possesses reciprocal symmetry, such that:
H12(k;n1,n2)=HT21(k;n2,n1), EQ. 1
Where H12 (k;n1,n2) is the M1(n2)×M2(n1) MIMO transfer function for the data downlinked from node 1 to node 2 over channel k, less possible observed timing and carrier offset between uplink and downlink paths,
and,
H21(k;n2,n1) s the M2(n2)×M1(n1) MIMO transfer function for the data uplink from node 2 to node 1 over channel k, and where ( )T denotes the matrix transpose operation.
In the preferred embodiment, this is effected by using the TDD protocol, and by sharing antennas during transmit and receive operations and performing appropriate transceiver calibration and compensation to remove substantive differences between transmit and receive system responses (this can include path gain-and-phase differences after the transmit/receive switch, but does not in general require compensation of [small] unequal observed timing and carrier offset between uplink and downlink paths). However, simplex, random-access packet, and other alternative methods are also disclosed and incorporated herein.
The network is further designed such that at each MIMO-capable node n with M(n) antennae, no more than M(n) other actively transmitting nodes are in node n's field of view, enabling node n to effect a substantively null-steering solution as part of its transmissions, such that each node belonging to a downlink receive set can steer independent nulls to every uplink receive node in its field of view during transmit and receive operations, and such that each node belonging to an uplink receive set can steer independent nulls to every downlink receive node in its field of view during transmit and receive operations.
The preferred embodiment also has means for incorporation of pilot data during transmission operations, and means for computationally efficient exploitation of that pilot data during subsequent reception operations. This is to enable transmitting nodes to unambiguously direct information to intended recipient nodes in the network; to enable receiving nodes to unambiguously identify information intended for them to receive; to enable nodes to rapidly develop substantively null-steering receive weights that maximize the signal-to-interference-and-noise ratio (SINR) attainable by the link, to enable nodes to reject interference intended for other nodes in the network, to enable nodes to remove effects of observed timing offset in the link, and to enable the nodes and network to develop quality statistics for use in subsequent decoding, error detection, and transmit power management operations.
The preferred embodiment prefers a network designed to create and support a condition of network reciprocity, where the uplink and downlink criteria are reciprocal at the network level. (
LEGO is enabled by exploiting substantive reciprocity of the internode channel responses, together with appropriate normalization of transmit power measures, to design uplink and downlink network quality metrics D21(W2,G1) and D12(W1,G2) that satisfy network reciprocity property:
D12(W,G)=D21(G*,W*) EQ. 2
where (W2,G1) and (W1,G2) represent the receive and transmit weights employed by all nodes in the network during uplink and downlink operations, respectively. If equation 1 holds, then equal network quality can be achieved in each link direction by setting G1=W1* and G2=W2*, such that each node use the receive combiner weights as transmit distribution weights during subsequent transmission operations, i.e., the network is preferentially designed and constrained such that each link is substantially reciprocal, such that the ad hoc network capacity measure can be made equal in both link directions by setting at both ends of the link:
g2(k,q)∝w2*(k,q) and g1(k,q)∝w1*(k,q)
where {g2(k,q), w1(k,q)} are the linear transmit and receive weights to transmit data d2(k,q) from node n2(q) to node n1(q) over channel k in the downlink, and where {g1(k,q),w2(k,q)} are the linear transmit and receive weights used to transmit data d1(k,q) from node n1(q) back to node n2(q) over equivalent channel k in the uplink; thereby allowing Eq. 1 to be satisfied for such links.
The invention further iteratively optimizes network quality (as defined by D12 and D21) over multiple frames, by first adapting combiner weights to locally optimize link (and therefore network) performance during receive operations, and then using Eq. 1A and the reciprocity property (Eq. 1) to further optimize network quality in the reverse direction over subsequent transmit operations.
The invention further improves on this approach by using Eq. 1 to scale each transmit vector, based on a partial linearization of the network quality metrics, to either minimize the total transmit power in the entire network subject to a network quality constraint, preferentially capacity, or maximize network quality, preferentially capacity, subject to a total transmit power constraint. This constraint is defined and managed as a control parameter that is updated by the network. The total transmit power at a given node is then reported as an output to the network.
By using target criteria such as (1) for a cellular network, a max-min capacity criterion subject to a power constraint, or (2) for a wireless LAN, a max-sum capacity that is subject to a power constraint, and using simple comparative operations in feedback for the network to optimize towards those criteria, this invention enables flexibility and stability for any given hardware and software combination that underlies a wireless electromagnetic communications network and improves, for the entire network and at each particular node thereof, the communication capacity and power requirements. Furthermore, the present form of the invention does not ignore but rather directly addresses and resolves both the overhead vs. content and the power vs. capacity conundrums which otherwise limit present-day state of the art approaches to optimization. It does this using the experienced environment as part of the direct feedback, rather than requiring additional control information or signaling that reduces content capacity.
When combined with the substantively null-steering approach described here, which helps to minimize the generated noise from all other signals sent from a node, the network power requirement for clear communication drops as the links effectively decouple; that is, the ‘other’ channels, since they are being null-steered, do not form part of the background noise against which the intended signal's power must be boosted to be accurately received by the intended recipient. (See
The LEGO power optimization and null-steering then feed back (reciprocally) into the requirements for the network and network's hardware at each node, inasmuch as the minimization of unused and unintentional interaction (or interference) reduces the precision and power necessary for frequency and other differentiation means at the node's transceiver, and reduces the number of antennae in each array by increasing the effective bandwidth within each multipath channel, by reducing the amount of bandwidth, frequency, time, or channel, or all of the above, that must be devoted to error avoidance or correction. That in turn simplifies the codec and other element designs for each node and lowers the cost of the transceiver front-ends.
The preferred embodiment of the invention employs a network of fully-adaptive PHY-IA MIMO Network Capable Transceivers, in which each transceiver implementing an upper PHY that performs transmit and receive TRANSEC, node signaling/detection protocol, transmit/receive beamforming, and receive-side node discovery and adaptation algorithms, and a lower PHY that can meet the needs of intermittent, burst packet communications such as VoIP.
Communication between source and destination nodes, defined by unique two-hex port addresses comprising the source and destination node addresses (SNA's and DNA's) for the packet transmission, is accomplished by routing traffic packets over at least one and possibly several routes or collections of sequential links between the source and destination node. Means for partitioning traffic data into individual data packets at the source node, and collecting data packets into traffic packets at the destination node, are accomplished in this invention using existing network routing protocols. For each combination of a transmitting node, a receiving node, and a communication channel (diversity link), the unique link address and identifying transmit node address and receive node address for the respective nodes are used as part of the messaging context.
Combining packet-specific, structural and origin/destination network information into a unified overhead allows implementation of orthoganal transformations of that overhead, through the use of specific power-of-two integer number of lower PHY (LPHY) symbols (which are preferably an OFDM waveform or PAM signal); and using a unique and identifying link address for each node-to-node link currently instantiated, which incorporates source, destination, and channel rank information, enables informational efficiency for short transmissions where otherwise structural and routing information might overweigh content, e.g. in each packet. Working within bounds (time intervals, frequency ranges, or transmission strengths) that guard against intrasystemic interference, the use of MIMO transformations and reciprocity-based pilot or signal weighting calculations for the correct weighting of signals transmitted and received, enables the individual packets and messages to adapt, in a bottom-up, flexible, and responsive fashion to the real-world dynamics of a continuously varying EM flux. Using adaptively-derived diversity weighting, the method and system can rapidly take advantage of reciprocity between each node pairings' transmit and receive channels to distinguish the desired signal from the general noise and potential interference. Upon RF reception at any node, downconversion and ADC operations on the diversity channels passes the incoming signal(s) through a set of inverting transformations that, for the desired incoming signal(s), strip off known structural elements and continuously updates the combiner weights to reflect the dynamically varying environmental and signal context, thereby continuously matching necessitated signal and waveform transformations to the environmental and signal effects and sources. By successive iterations of the transmit and receive adaptation algorithm each node can have its transceiver adapt its multiport combiner and distribution weights to the eigenmodes (left and right eigenvectors) of their MIMO internode channel response, so that the resultant fully adaptive link can approach the Shannon capacity of the MIMO communication channel, regardless of the rank or distribution of the eigenvalues of that channel.
In addition, the fully adaptive system provides an automatic power control mechanism (LEGO Algorithm) that can be used to maximize capacity (high throughput applications) or minimize transmit power (LPD applications), depending on the requirements of the system at any point during a mission.
The resultant network is able to pass data with high spectral efficiency relative to non-MIMO networks, or to meet specified packet transmission rates at much lower power levels relative to non-MIMO networks, due to its ability to pass data over multiple time-and-frequency coincident links and routes, and to exploit the much lower pathloss between intermediate nodes in the network. Moreover, the network is able to provide this performance in the complete lack of any opportunistic multipath (although that multipath can be exploited if it is available).
ACK Acknowledgement
ADC Analog-to-Digital Conversion
ADSL Asynchronous Digital Subscriber Line
AGC Automatic Gain Control
BS Base Station
BER Bit Error Rate
BW Bandwidth
CBR Committed Bit-Rate service
CDMA Code Division Multiple Access
CE&FC RWA Computationally Efficient And Fast-Converging Receive Weight Algorithm
CMRS Cellular Mobile Radio Systems
CODEC Encoder-decoder, particularly when used for channel coding
CPU Central Processing Unit
CR Channel Reciprocity
DAC Digital-to-Analog Conversion
DEMOD Demodulator
DMT Digital MultiTone,
DSL Digital Signal Loss
DMX De-multiplexer
DOF Degrees of Freedom
DSP Digital Signal Processing
EDB Error-Detection Block
EEPROM Electronically Erasable, Programmable Read Only Memory
FDD Frequency Division Duplex
FDMA Frequency Division Multiple Access
FFT Fast Fourier Transform(s)
FPGA Freely Programmable Gate Array
GPS Global Positioning Satellites
GSM Global System for Mobile Communications
LEGO Locally Enabled Global Optimization
LMS Least Mean-Square
LNA Low Noise Amplifier
LS Least-Squares (An alternative form can be ‘matrix inversion’)
MAC Media Access Control
MGSO Modified Gram-Schmidt Orthogonalization (most popular means for taking QRD)
MOD Modulator
MIMO Multiple-Input, Multiple-Output
MMSE Minimum Mean-Square Error
MSE Mean-Square Error
MT Multitone
MUX Multiplex, Multiplexer
NACK Negative acknowledgement & request for retransmission
NAK Negative Acknowledgement
OFDM Orthogonal Frequency Division Multiplexing
PAL Programmable Array Logic
PDA Personal Data Assistant
PHS Personal Handiphone System
PHY Physical layer
PMP Point-to-Multipoint (An alternative form can be ‘broadcast’)
PSTN Public Switched Telephone Network
PSK Phase-Shift Key
π/4 QPSK (pi/4)—Quadrature Phase Shift Key
π/4 DQPSK (pi/4)—Digital Quadrature Phase Shift Key
PTP Point-to-Point
QAM Quadrature Amplitude Modulation
QoS Quality of Service
QRD Matric {Q,R} decomposition (see, MGSO)
RF Radio Frequency
RTS Request To Send, recipient ready for traffic
SDMA Spatial Division Multiple Access
SINR Signal to Noise Ration (An alternative form can be S/N)
SOVA Soft-Optimized, Viterbi Algorithm
SU Subscriber Unit
TCM Trellis-Coded-Modulation
TCP/IP Transmission Control Protocol/Internet Protocol
TDMA Time Division Multiple Access
TDD Time Division Duplex
T/R Transmit/Receive (also Tx/Rx)
UBR Uncommitted Bit-Rate (services)
ZE-UBR Zero-error, Uncommitted Bit-Rate (services)
Groundwork: the Network as a Dynamic Connected Set
A network is generally viewed as the combination of a set of nodes (where transmissions originate and are received) and the connections between those nodes through which the information is flowing.
Because each node may both transmit (send) and receive, and because the connections amongst the set of nodes may change over time, the network is best thought of as a dynamic structure, i.e. one that is constantly shifting yet which still occupies the same general ‘space’ in the communications world. While traditional broadcast networks, or PTP or PMP networks generally tried to ‘fix’ at least the originating node, a MIMO network begins with the presumption that the communications are dynamically allocated amongst the nodes and throughout the network. In the present embodiment of the invention, diversity in spatial, spectral, temporal, or polarization attributes of the potential channels are not seen as variations that must be controlled or limited, but as opportunities for enhancing performance.
Limitations of Existing Art
The approaches currently described in the field, especially in Raleigh and Cioffi, G. Raleigh, J. Cioffi, “Spatio-Temporal Coding for Wireless Communications,” in Proc. 1996 Global Telecommunications Conf., November 1996, pp 1809-1814), and in Foschini and Gans (G. Foschini, M. Gans, “On Limits of Wireless Communication in a Fading Environment When Using Multiple Antennas”, Wireless Personal Comm., March 1998, Vol. 6, Nol. 3, pp. 311-355), require additional hardware at each node comprising one end of a channel per diversity path to exploit that diversity path. This creates a geometric growth in the hardware complexity for each particular node, and a linear growth in cost for each diverse path exploited by a given network, that rapidly renders any network attempting to exploit such diversity uneconomic. Moreover, such an approach ‘muddies its own stream’ in that it reduces the capacity increase by the power increase needed to power the more complex transceiver. Exploitation of this multipath approach requires both high power (to permit data transport over the relatively weaker additional diversity path) and complex codecs (to permit data transports at high rates on the dominant path by filtering out the diversity path transmissions). To the extent that the nodes differ in their antennae mix, this approach complicates the administration and management of the network by constraining the potential path exploitation to previously-known or approved channels where the required equipment for each diverse path is known to exist.
Spatially distributed networks overcome this particular limitation by exploiting the inherent diversity between internode channel responses in the network. This diversity exists regardless of any multipath present on any individual path in the network, i.e. it does not require high levels of opportunistic multipath to be exploitable by the system. Moreover, such spatial diversity can be designed into the network by careful choice of topologies for the nodes during the deployment process, in order to provide linear growth in capacity as transceivers are added to the network. As a side benefit, the network can spatially excise transmissions from compromised nodes and emitters, allowing secure, high quality service in environments with external interference.
A downside to such an approach is its obvious weakness to unexpected growth, dynamic changes in topology (from mobile, transitory, or transient nodes), or dramatic changes in relative channel densities. Unlike the present form of the invention, such an approach does not handle well unplanned-for competition, environmental changes, or readily exploit opportunities arising from surprisingly (i.e. unplanned for) good network performance.
MIMO Networks: Shapes and Spaces
The complex MIMO environment and multiple dimensions of differentiation (spatial, frequency, time, code), the physical geography of any network (ring, star, mesh), the physical geographies of the surrounding terrain (creating the multipaths) and the other wireless signals from outside the network, and the internal network environment (of traffic patterns and node differences) create a diversity explosion.
To create and manage optimal network capacity, the preferred embodiment creates a network topology that enforces a constraint where each node with M(n) antennae has ≦M(n) other nodes in its view with whom it communicates at any particular interval of time. This may take the form of a ring (see
While the preferred embodiment works with reciprocal subspaces, wherein the network maintains reciprocity according to Eq. 1 between nodes over all links joining them, some parameters may be allowed to vary and create asymmetric spaces. For example, in a carrier offset case, the channel responses are actually invariant but for the complex scalar sinusoid which creates the frequency offset; physically, this is a non-reciprocal link but logically it remains (assuming signal content density on both sides is kept equal) a substantively reciprocal link. Other adaptation means are permissible as long as the network design rule of Eq. 1 is kept as a high priority.
One major difference in the present embodiment of the invention from prior art is that by making the control and feedback aspects part of the signal encoding process, and thereby eliminating or at least reducing the need for a separate channel(s) for control and feedback, the network content overhead is reduced and an additional range within the signal dimension is available for signal content. The LEGO reduction to single, or small, bit sized power management signals can be similarly echoed for other network management, depending on the target objective the network elects.
Furthermore, because the MIMO and LEGO approach described herein is usable in any network topology, and with existing protocols and schema (PSK/QAM; FDD; CDMA, including modulation-on-symbol, or synchronous, CDMA; TDMA; SDMA, etc.) the network can adapt to a diverse environment of users rather than requiring all to have the identical hardware, software, and standards.
The diversity of transmission and reception at all nodes in the network, rather than just at a subset of hub nodes or BSs, means that every node can use in its local environment any redundancy in transmission or reception of data over multiple channels, whether they be spatial, polarization, spectral, temporal, or any combination thereof. The maximum use can be made of all available (i.e. unused by others) signaling lacunae, with the nodes adaptively adjusting to the traffic and external environmental conditions according to the objectives set by the network.
Furthermore, the present embodiment of the invention does not require a preliminary calibration of the transceiver array, the communications channel, or geographic site as do many approaches used in the prior art. The continuous feedback and rapid convergence of the approach allow for flexibility and adaptivity that will permit correction of miscalibrated data, when the miscalibration represents a no-longer valid model of the environment for the receiving node.
Additionally, the MIMO network of the present embodiment is adaptive to channel response changes due to network point failures. This includes: the ability to survive element failure at individual nodes (i.e. one antenna, or transceiver, fails) without loss of communication to that node (though it may incur possible loss of capacity); the ability to survive failure of links without loss of communication to that node (e.g., by routing data through other paths); the ability to survive failure of node (all links terminating at that node) without unduly affecting connectivity or capacity of network; and the ability to achieve network reliability that is higher than reliability of any node in that network. The network will automatically adjust itself to optimal performance in event of any of these failures; potentially by rerouting active links based on available SINR experienced at that link.
Application Areas and Advantages: MIMO
The incorporation of the control and feedback signal as part of the process rather than as discrete, separate, and particular parts of the signal, can decrease the complexity of apparatus by removing the need for a separate channel for network control. It also can decrease the complexity of the processing by removing the need for particular dedication of a time aspect of the reception, or by removing the need for additional detection and interpretation of control signal from other content through either software or hardware. Moreover, such incorporation also integrates the entire aspect of power management and control into the signaling process rather than artificially and needlessly separating it from the network dynamics. This integration allows both capacity and power control to cooperatively handle packet acknowledgment, signal synchronization, and transmit/receive functionality at each node, and to optimize their conjoined functionality to the needs of the environment, the user, or the node rather than being constrained to disparate, pre-set and non-dynamic dictates by network administration that are only responsive to the real world environment to the extent that the system designers' assumptions accurately modeled the real-world and unknowable complexities.
Because the control and feedback signaling is incorporated into the process, the present form of the invention does not impose overhead constraints or capacity demands upon the network to nearly the same degree as the prior art does. For a given infrastructure and environment, therefore, the present form of the invention provides increased capacity and performance through dynamic, and self-moderating signal processing algorithms, with a minimal overhead.
Additionally, because the method does not require a strict hierarchical division between Base Station and Subscriber Unit nodes, but rather adapts to the diversity of reception and transmission at each particular node depending on its then-current environmental context, the method allows for rapid and responsive deployment of mixed hardware units being conditioned by factors external to the network, such as user choice or economic limitations.
Unlike prior art, the present form of the invention will work with each of CDMA, FDD, TDMA, SDMA, and PSK/QAM implementations, and with any combination thereof. Because the present form of the invention will work with diverse environments, where the diversity may come from within the network (rather than from sources external to it), this protects users and companies' investments in prior infrastructure and avoids creating either a ‘captive service market’ subject to crippling and sudden innovation, or creating a network which will suffer when a Christiansen-style disruptive technology advance arrives. Moreover, diversity reception, which is the redundant transmission and reception of data over multiple channels (whether the diversity comes in spatial, polarization, spectral, or temporal form, or any combination thereof), permits successful operation in environmental conditions which would otherwise block any particular channel or perfect subset of channels. This means that the present form of the invention will continue to operate in dirty, bursty, or difficult conditions, whether the impact of the negative force is on the nodes or the external environment.
As such, there are a number of potential implementations which immediately become feasible for a dynamically adaptive network, in the military and security fields. These include military and civilian applications where individual unit or node failure can be anticipated and therefore must not bring down the network, and where environmental conditions can become disruptive for particular nodes or links. These would also include support and exploration applications where the external environment (including node location) and network internal environment (traffic, connectivity) may change over time, as the component nodes move and change capabilities and capacities.
Among the effects which enhance the ready establishment and dynamic use of security advantages through the present form of the invention are the three-layer pilot signal (network mask plus originator mask plus recipient mask) detailed below (See
Unlike the prior art, the present form of the invention will also allow for optional specialization (e.g. in transmission, reception, flow-through channelization) at any particular node in a dynamic fashion, thereby allowing the network as a whole to adapt to transient environmental fluctuations without concomitant alteration in on-the-ground hardware or in-the-system software alterations. Such ready adaptivity increases the total cost-effectiveness, as well as the dynamic stability for the entire network.
Unlike prior art, the present form of the invention supports diversity reception and transmission at all nodes in the network. This creates a level of flexibility, adaptivity to environmental or network changes, and dynamic stability which increases the ready scalability over multiple distinct approaches simultaneously or serially accepted by the network. Since the core reciprocity and protocols can be used by distinctly different hardware and signals, the present form of the invention permits local accretive growth rather than demanding top-down, network-wide initial standardization, thereby decreasing the capital and planning cost for implementing or changing a network.
Another advantage of the present form of the invention is that it permits shared antenna usage amongst multiple nodes, thereby decreasing the number of antenna necessary for any given node to attain a particular capacity, and thereby decreasing for the network as a whole the cost and complexity required for that same level of capacity. Furthermore, it also permits any set of nodes to use a diversity of channels without requiring an increase in the antenna or internal complexity (in both hardware and software) at every node in said set of nodes.
A further advantage of the present form of the invention is the ability to adaptively select and use ad-hoc, single-frequency networks on all or part of the network, under conditions when network traffic is ‘bursty’, that is, when there are significant disparities between the high and low content volumes of traffic being communicated amongst that part of the network.
A particularly significant advantage of the present form of the invention is that using the reciprocity equation equalizes the processing or duty cycle for message transmission and reception across both directions of a link, thereby lowering the processing imbalance which otherwise might be created between transmission and reception modes. This in turn reduces the average complexity which must be built into each particular node by decreasing the maximal capacity it must be created to handle for an overall network minimal capacity average.
Another advantage of the present form of the invention is that, unlike much of the prior art, the present form of the invention will work in uncalibrated areas where the environmental context is either previously unknown or altered from previous conditions. This allows for rapid, uniphase adoption and expansion in any given area without requiring prior to the adoption the precise calculation of all environmental effects upon transmissions and receptions within said area at all planned or possible node locations. This further allows the adoption and use for transient, or mobile, nodes in areas without requiring all possible combinations of channel responses amongst said nodes first being calibrated and then said channel responses matched to current conditions, or constrained to pre-set limitations.
Another advantage of the present form of the invention is that it provides rapid correction for miscalibrated data, thereby reducing the cost of inaccurate measurement, human or other measurement error, or incorrect calibration calculations. This in turn reduces the overhead and planning required for adaptation for any given network to a particular environment, either initially or as the environment changes over time, as the channel responses in the real world can be readily adapted to.
A concomitant advantage of the present form of the invention is the rapid and dynamic adaptation to channel response changes when a network failure, at any particular node or sub-set of nodes, occurs. This greatly increases the stability and durability of any network incorporating the present form of the invention without the level of cost, complexity, or duplication required by the present state of the art. Amongst the advantages conveyed are the ability for the network to survive partial failure at any particular node without being forced to drop or lose that node (i.e. maintaining maximal attainable capacity between that node and all others to which is can communicate), the ability to survive the total lose of any particular node, by sharing the signal traffic amongst alternative channels. The present form of the invention also permits the rerouting of active links around ‘lost’ or ‘damaged’ nodes without human intervention by adherence to the new reciprocity measurements. And the increase in network stability to exceed not just the reliability of each particular node, but the average reliability of all nodes for, while any subset of nodes still remains operable, the maximal network capacity for that set can be maintained. This is unlike the present state of the art, where if 50% of the nodes of a network fail then the average network communication drops to zero, as all the channels lose one half of their pairs. Furthermore, the present form of the invention enables the network to automatically adjust itself and its optimal performance in the event of any partial failure without requiring human intervention, thereby decreasing the cost and increasing the responsiveness of the network. Even more important is the fact that, upon incremental re-instatement or restoration of particular node or channel function, network optimization continually advances without manual re-establishment.
Another advantage of the present form of the invention is that it minimizes the complexity, and increases the accuracy, of the signal weight update operation at each particular node and for the network as a whole.
Another advantage of the present form of the invention is that it provides a computationally efficient mechanization of cross-correlation operations for both nodes and channels across and within the network. As the number of signals simultaneously processed on a single time-frequency channel grows, the marginal complexity increase caused by addition of those signals drops, for fast adaptation methods such as autocorrelation approaches, e.g. inverse-based or least-squares). This is because the Digital Signal Processing (DSP) cycles needed for high-complexity operations in fast techniques, such as matrix computations, QR decompositions, or data whitening operations common to DSP processing, can be amortized over the larger number of signals. The higher overhead of hardware and software complexity needed to handle signal complexity thereby is lowered on a per-signal basis the greater the complexity actually used by the system. For certain techniques such as pilot-based or least-squares signal weighting, the fast techniques become less complex than the current conventional approaches such as Least-Mean-Squares or stochastic gradient, wherein the overhead remains indifferent to the increasing complexity of the signals being processed. When the number of signals that must be processed is equal to one-half the number of combiner weights used at an adaptive receiver, for example, then the crossover in overhead complexity vs. speed occurs between least squares and least mean squares.
Because the present form of the invention is dynamically adaptive, it can use any subordinate portion (in time, channels, or network subset) of the process wherein a ‘reciprocal subspace’ exists to implement its full value. Even though the parameters of the signal processing may vary between the uplink (transmission) and downlink (reception) phases between any two nodes on any given channel or link, to the extent that they overlap such a reciprocal subspace can be effectuated and used. For example, a reciprocal subspace can be created where there is a carrier offset, where the channel responses are distinguished solely by a scalar complex sinusoid (e.g. a frequency offset), between the two nodes, regardless of which is, at any particular moment, transmitting or receiving.
Since the present form of the invention with its non-orthogonal multitone capability allows the addition of mobile, transient, or temporary nodes to any network, it creates a system that can manage and provision any combination of fixed, portable, low mobility, and high mobility nodes and links. The capacity constraints being node and channel specific rather than network delimited also permits the heterogeneous combination of differing capacities, thereby allowing peripheral distinctiveness, creating the potential for a system with a hierarchy of nodes including high-function, base-function, and limited-function or even single-function (e.g. appliance) nodes.
The present form of the invention permits the ability to achieve nearly linear increases in capability, even superlinear increases (in dirty environments) for increases in infrastructure, namely the RF transceiver capacities within the network. This is a significant advance over the prior state of the art for PTP networks which achieve sublinear capacity growth with network infrastructure growth.
In non-fully loaded networks using the present form of the invention, the MIMO connectivity can provide sharply higher data rates to individual channels or nodes where the additional information flow, to the maximal capacity of the particular nodes, is routed through nodes which have intended reception or transmission capacity available. This is a ‘water balancing’ approach to traffic maximization available only when multiple rather than single path capabilities are established through a network, or any network structure that instantiates fixed bottlenecks (e.g. ‘star’ or ‘hub’ topologies, BS PMP networks, or fixed-channel PTP networks).
Additionally, the reciprocity approach enables an automatic and dynamic load sharing amongst the channels and nodes which minimizes bottlenecks or, in the military or security environment, desirable targets of opportunity for ‘hot centers’ of traffic. Commercially this is more valuable by reducing the power and complexity requirements of what in PTP and PMP networks are BSs to attain a given network capacity and power efficiency.
The preferred embodiment of the present form of the invention includes a number of interacting and synergistic elements, both in hardware and in operational software. The preferred embodiment, as a network, will incorporate particular functional elements at individual nodes, as well as overall systemic features which may not be shared by or incorporated in the hardware of each particular node (i.e. there may exist specialization amongst the nodes). As stated in the summary, each node preferentially has an antennae array; multiple, multitone, transceivers (one per antenna); and constrains itself to reciprocal uplinks and downlinks (
The first preference is that the transmission element be a multi-tone front end, using OFDM with cyclic prefixes at fixed terminals (generally, BS) (
Each node of the network incorporates a MIMO transceiver.
Each MIMO transceiver possesses an antennae array where the antennae are spatially separated and the antennae array itself is preferentially circularly symmetric (
In an alternative embodiment the transceiver sends the transmission signal through Butler Mode Forming circuitry (
Having passed through the Butler Mode Forming circuitry, the transmission is then sent through the transmission switch (
Further transmission switch processing then hands off the transmission to the frequency translator (
From the transmission switch the transmission goes to an ADC bank (
Then from the ADC bank the transmission flows through a Multitone Demodulator Bank (
From the Multitone Demodulator Bank the Rx data is passed to circuitry for mapping the received broadband multitone signal into separated, narrowband frequency channels and time slots (
An outgoing transmission signal experiences the reverse of the above process; having been mapped to tones and RF feeds (
The transmission switch throughout is controlled such that baseband link distribution of the outgoing signals takes place such that energy is distributed over the multiple RF feeds on each channel, steering up to Kfeed beams and nulls independently on each FDMA channel in order to enhance node and network capacity and coverage. This control further greatly reduces the link fade margin and that node's PA requirements.
The particular Multitone MOD and DEMOD elements (
The 426 bins form 13 channels and 426 tones, (
For non-fixed embodiments, the timing modifications may be varied. The signal being processed is handed first to a MUX where an element-multiply with a Tx or Rx (for transmit or receive, respectively) window is performed. The guard time is retained to serve as dead time between signals, effectively punctuating them. The high-mobility embodiment halves the number of bins, doubling the average bin size, and uses duplication to increase QoS within the multitone. (
The next stage through the MIMO transceiver is the incorporation (on the transmission side) or interpretation (on the reception side) of the QAM/PSK symbols, prior to the signal's passing through the MIMO transceiver exits (if being transmitted) or enters (if being received) through a Link codec. Each FDMA channel will separate through the codec into 1 through M links, and each Link codec will incorporate tone equalization and ITI remove as necessary. The Link codec also includes SOVA bit recovery, error coding and error detection, and package fragment retransmission methodologies.
An optional alternative embodiment would at this point further include tone/slot interleaving (for the reception) or deleaving (for the transmission). A further optional alternative embodiment would replace the TCM codec and SOVA decoder with a Turbo codec.
Another optional alternative will incorporate dual-polarization. (See
The Transceiver DSP backend for the preferred embodiment is detailed in
The LEGO gain adaptation element at each node enables the network to optimally balance the power use against capacity for each channel, link, and node, and hence for the network as a whole.
A capacity objective β for a particular node 2 receiving from another node 1 is set as the target to be achieved by node 2. Node 2 solves the constrained local optimization problem:
where π1(q) is the SU (user 1 node) transmit power for link number q,
γ(q) is the signal to interference noise ratio (SINR) seen at the output of the beamformer,
1□s a vector of all 1s,
and □
π1 is a vector whose qth element is p1(q).
The aggregate set Q(m) contains a set of links that are grouped together for the purpose of measuring capacity flows through those links.
An example of this would be if SU had connections to multiple BSs, and we were primarily concerned with the total information flow into and out of a given node. In this case all of the links that connected to that node would be in the same aggregate set. Also in this description, we have adopted the convention that each transmit path from a transmitter to a receiver for a given narrow-band frequency channel is given a separate link number, even if the BS and SU are the same. Thus multiple transmit modes, that say exploit multipath or polarization diversity, are each given different link numbers, even though the source and destination nodes might be identical. Moreover, if a BS/SU pair transmit over multiple frequency channels, then each channel is given a separate link number. (This simplifies notation considerably.)
An example of this is shown in
be dynamically combined into proper subsets of transmit uplink and receive uplink. The choice of aggregate sets can be arbitrary, provided no link is in multiple aggregate sets. However in a preferred embodiment, the aggregate sets are links that share a common node and hence common, readily available channel parameters.
The downlink objective function can be written as:
min Σqπ2(q)=1Tπ2 such that EQ. 5
ΣqεQ(m)log(1+γ(q))≧β(m) EQ. 6
The required feasibility condition, that ΣqεQ(m) π1(q)≦R1(m) is reported to the network, and in the preferred embodiment, reported to a network controller, so that β(m) can be modified as needed to stay within the constraints.
In an alternative embodiment, the capacity constraints β(m) are determined in advance for each aggregate set, based on known QoS requirements for given nodes or group of nodes. The objective function then seeks to minimize the total power in the network as suggested by EQ. 4.
By defining the noise normalized power transfer matrix by:
Prt(q,j)=|wHr(q)Hrt(q,j)gt(j)|2, EQ. 7
where Wr(q) is a receiver weight vector for link q, and,
gt(j) is the transmit weight vector for link j.
By unit normalizing the receive and transmit weights with respect to the background interference autocorrelation matrix, the local model can state:
wrH(q)Ri
enabling the nodal model to express the SINR equation as:
Accordingly, a matrix condition can be defined on the range of possible output SINRs; and from this, πt has a feasible, that is non-negative solution, if and only if:
ρ(δ(γ)(Prt−δ(Prt))<1, EQ. 9
where ρ(M) is the spectral radius of a matrix M,
the non-negative power transfer matrix Prt has qj'th element given in EQ. 7,
δ(γ) is a diagonal matrix whose q′th element is γ(q)
and
δ(Prt) is a diagonal matrix with the same diagonal as Prt.
The weight normalization in EQ. 52, and the assumption of reciprocal channel matrices leads to the reciprocity equation (EQ. 1), and the fact that the uplink and downlink objective functions in EQ. 3 and EQ. 4 are identical for the same target SINRs.
Various means for solving the optimization in EQ. 3 exist; the preferred embodiment uses a very simple approximation for γ(q), as very weak constraints to the transmit powers are all that are needed to yield objective functions which satisfy the reciprocity equation (EQ. 2).
Another approach can take advantage of the case where all the aggregate sets contains a single link, and we have non-negligible environmental noise or interference. For smaller networks, all the channel transfer gains in the matrices P12 and P21 are estimated and the transmit powers are computed as Perron vectors from:
In this case a simple power constraint is imposed upon the transmit powers, so that they remain feasible. The optimization is alternating directions, first the weights are optimized, then the powers are obtained from the Perron vectors, and the process is repeated.
Another embodiment assumes effectively that the denominator in Eq. 8 remains approximately constant even after changes to the power levels in other nodes in the network (hence the local optimization approach), because the beamformer weights in the network (transmit and receive) in the MIMO approach will attempt to cancel the co-channel interference in the network, making it insensitive to power level changes of the interferers. The denominator in Eq. 8 represents the post beamforming interference seen by the receiver associated with link q for the forward link (downlink) if r=1, and the reverse link if r=2.
With this approximation, and a rewriting of Eq. 8 (for the uplink) to:
is the post beamforming interference energy, and is assumed constant for the adjustment interval for current transmit power values, the node can solve EQ. 3 in closed form using classic water filling arguments based on Lagrange multipliers. A similar equation is established for the downlink.
An alternative embodiment of Eq. 11 is to measure, provide, and use actual information for additional, available, or important terms in the denominator of Eq. 8 and to incorporate them into Eq. 12, and then rather than closed form use successive applications thereof to the modified problem using local data.
Another alternative embodiment is to solve, at each node, the constrained optimization problem:
using the approximation in Eq. 11, which is a water-filling solution similar to that described above for Eq. 3. This solution requires a high-level network optimizer to control the power constraints, R1(q), to drive the network to a max-min solution.
The preferred embodiment, however, solves the local problem by attempting to minimize the total power as a function of the target output SINR. The output SINR will be the ratio of square of the channel transfer gain times the transmit power, divided by the interference power seen at the output of the beamformer, where:
γ(q)=|h2(q)|π1(q)/i2(q) EQ. 15
γ(q)=|h2(q)|π2(q)/i1(q) EQ. 16
where |h(q)|2 is the square of the channel transfer gain,
π1(q) is the transmit power for link q during the reverse link or uplink transmission,
π2(q) is the transmit power for link q during forward link or downlink transmission,
i1(q) is the interference power seen at the output of the beamformer used by the SU associated with link q,
and,
i2(q) is the interference power seen at the output of the beamformer used by the BD associated with link q.
This makes the output SINR a function of all the transmit powers at all the other SUs in the network. Additionally, by normalizing the beamforming weights with respect to the background interference, it is possible to maintain the reciprocity equation even in the presence of arbitrary interference and noise, due to non-cooperative signal sources, such as jammers or co-channel communication devices. Maximizing the SINR yields optimal receiver weights that can remove the effect of jammers and co-channel interferers. The reciprocity equation insures that the optimal transmit weights puts substantive nulls in the direction of these same co-channel interferers. For military applications, this implies that the network reduces it's probability of detection and interception, and for co-channel communication systems, it reduces it's transmitted interference, and is effectively a ‘good neighbor’ permitting system deployment in otherwise unacceptable environments. Commercially, this allows a network employing the present embodiment of this invention to cope with competitive, impinging, wireless network nodes and transmissions.
It can be shown that there is a 1-1 mapping between all the transmit powers and all of the output SINRs, i.e. there exists a vector valued function F1 such that F1(γ)=π1.
The function has an inverse so that F1−1(π1)=γ. A key result that is exploited by this embodiment is the fact that if the channels are reciprocal, then the objective functions, and the constraint set imposed by (1) is identical as a function of γ for both the uplink and downlink objective functions. Mathematically this means these objective functions can be stated in general terms as:
f(γ)=1TF1(γ)=1TF2(γ), EQ. 17
where π2=F2(γ) is the mapping between the SINRs and the BS transmit powers.
In the preferred embodiment, each node uses the above as it defines and generates its local model as follows:
Given an initial γ0 generate the model,
L(γ,g,β)=gTγ EQ. 20
ΣqεQ(m)log(1+γ(q))≧β(m) EQ. 21
g=∇γf(γ0) EQ. 22
where L(γ,g,β) is a linearized model of the objective function,
gTγ□ is an inner product between the gradient of the objective function and a set of target SINRs,
ΣqεQ(m)log(1+γ(q))≧β(m)□ is the capacity constraint for aggregate set m,
and,
g=∇γf(γ0) is the gradient of the objective function (the total transmit power) as a function of the target SINRs.
The new γα is updated from
γ*=arg minγL(γ,g,β) EQ. 23
γα=γ0+α(γ*−γ0) EQ. 24
The constant α is chosen between 0 and 1 and dampens the update step of the algorithm. This determines a target SINR that the algorithm adapts to. The update for the transmit power for link q becomes,
π2(q)=γαi1(q)/h(q)2 EQ. 25
π1(q)=γαi2(q)//h(q)2 EQ. 26
Where i1(q) and i2(q) are the post beamforming interference power seen at the SU and the BS respectively for link q.
The present embodiment of this invention uses advantageously the fact that the qth element of the gradient of the objective function can be written as the product of the interference powers divided by the square of the transfer gain:
{∇γf(γ0)}q=i1(q)i2(q)//|h(q)|2. EQ. 27
The transmit power update relationship in Eq. 25 and Eq. 26 can be applied repeatedly for a fixed feasible γα and the convergence of π1→F1(γα) is guaranteed. In fact some assert this convergence will be guaranteed if we optimize the receive weights at each iteration. (See Visotsky, E; Madhow, U, “Optimum Beamforming Using Transmit Antenna Arrays”, Vehicular Technology Conference, 1999 IEEE 4th, Volume 1, pp 851-856, though he only considered the effects in a Rank 1 channel, that is a single narrowband rather than a MIMO channel.) A similar statement holds for π2→F2(γα). In an alternative embodiment where the proper relationship is unknown, or dynamically changing, then a suitably long block of N samples is used to establish the relationship, where N is either 4 times the number of antennae or 128, whichever is larger, with the result being used to update the receive weights at each end of the link, optimize the local model in Eq. 23 and Eq. 24, and then apply Eq. 25 and Eq. 26.
The algorithm used in the preferred embodiment enables the network, and local nodes thereof, to attain several important results. First, for each aggregate set m, the optimization of the local model(s) at each node(s) completely decouples the network optimization problem to an independent (set) of local problem(s) that is solved among the aggregate set links. Accordingly, within a given aggregate set, we inherit the network objective function model:
Lm(γ,g,β)≡ΣqεQ(m)gqγ(q) EQ. 28
ΣqεQ(m)log(1+γ(q))≧β(m) EQ. 29
gq=i1(q)i2(q)//|h(q)|2 EQ. 30
where Lm(γ,g,β) is the sum of the separable, linearized, objective functions corresponding to the aggregate set number m, where each localized objective function depends only on variables that pertain to the given aggregate set,
gqγ(q) is the product of the q′th element of the gradient vector with the SINR for link q,
gq is the q′th element of the gradient vector,
and,
|h(q)|2 is the square of the channel transfer gain from the transmit beamformer, through the channel to the output beamformer (not including the transmit power).
Second, this approach eliminates matrix channel estimation as a necessary step, as solving the local problem only requires that an estimate of the post beamforming interference power, a single real number for each link, be transmitted to the other end of the link, or in another embodiment to the node assigned to computing the transmit powers for a given aggregate set. For each link, a single real number, the transmit power, is then propagated back to the transmitter. This is true even for networks with large rank MIMO channel matrices.
Third, the optimization problem, which is stated in general terms in Eq. 17, when you plug in a formula for π as a function of γ into the objective function, i.e. 1TFr(γ) for the SINR to power mapping Fr(γ), is reduced from a complex and potentially unsolvable problem to one that has a simple closed form solution, and thus can use a well known water filling problem seen in classical information theory (see T. Cover, T. Joy, Elements of Information Theory, Wiley; 1991); Matthew Bromberg and Brian Agee, “The LEGO approach for achieving max-min capacity in reciprocal multipoint networks,” in Proceedings of the Thirty Fourth Asilomar Conf. Signals, Systems, and Computers, October 2000.
Fourth, even when starting from non-feasible starting points, the algorithm rapidly converges; in the preferred embodiment, where all parameters are updated after every receive block, it converges to a fixed point within the vicinity of the optimal solution; and in an alternative embodiment, where the γα vector is held fixed until π1→F1(γα) and π2→F2(γα) before updating the weights and updating γα again.
A figure illustrating the computational tasks at the BS and the SU for a given link q is shown in
In the preferred embodiment, only one side of the link need perform the power management computations. One of the principle advantages and strengths of the present embodiment of the invention is that it replaces half of the prior art's explicit, dual computations with an implicit computation that is performed by the physical transmission of data, which generates the real-world interference (and thus interference values) used by the power control algorithm.
The estimation of the transfer gains and the post beamforming interference power is done efficiently in the preferred embodiment with simple least squares estimation techniques.
The problem of estimating the transfer gains and the post beamforming interference power (in the preferred embodiment, by using a least squares algorithm) is equivalent to solving for the transfer gain h as follows:
y(n)=hgs(n)+ε(n) EQ. 31
where y(n) is the output of the beamformer at the time sample, h≈wHr(q)H21(q,q)gt(q), whose square modulus is Prt(q,q),
wr(q) is the receive weight vector for link q,
gt(q) is transmit weight vector for link q,
g is the square root of the transmit power πt(q),
s(n) is the transmitted complex symbol at time sample n,
and
ε(n) represents all of the remaining co-channel interference and noise. (Indexing is dropped to avoid clutter.) Then y(n) is defined as the output after applying the unit normalized despread weights to the received data. This is simply the usual beamformer output divided by the norm of the despread weights with respect to the noise covariance matrix; and for many applications, this will be a scaled multiple of the identity matrix.
Using a block of N samples of data, h is then estimated as:
where h is the channel transfer gain,
s*(n) is the conjugate of s(n),
y(n) is the output of the beamformer at the time sample n,
and,
s(n) is the transmitted complex symbol at time sample n.
From this an estimation of the residual interference power, Rε, which is identified with i1 (q) in Eq. 11 by:
where
gh is the product of the transmit gain and the post-beamforming channel gain.
The knowledge of the transmitted data symbols s(n) in the preferred embodiment comes from using remodulated symbols at the output of the codec. Alternative embodiments use the output of a property restoral algorithm used in a blind beamforming algorithm such as constant modulus or constellation restoral, or by using a training sequence explicitly transmitted to train beamforming weights and asset the power management algorithms, or other means known to the art. This information, and the knowledge of the data transmit power values it π1(q) will be at the receiver and can be transmitted to the transmitter as part of a data link layer message; and if the processing occurs over fairly large blocks of data the transfer consumes only a small portion of the available bandwidth. Means for handling the case when a transmit mode is shut off, so that one of the π1(q)=0, include removing the index (q) from the optimization procedure and making no channel measurements.
In the preferred embodiment, a link level optimizer and decision algorithm (See
In an alternative embodiment, the solution to Eq. 3 is implemented by using a variety of Lagrange multiplier techniques. In other alternative embodiments, the solution to Eq. 3 is implemented by using a variety of penalty function techniques. All of these embody techniques known to the art for solving the local problem. One such alternative takes the derivative of γ(q) with respect to π1 and uses the Kronecker-Delta function and the weighted background noise; in separate alternative embodiments, the noise term can be neglected or normalized to one. An approximation uses the receive weights, particularly when null-steering efforts are being made, and as the optimal solution will have weights that approach the singular vectors of the interference-whitened MIMO channel response. In the situation where the links of a given aggregate set Q(m) are all connected to a single node in the network, all information pertaining to the subchannels and propagation modes of the MIMO channel associated with that node are available, hence the norm squared transfer gain P21(q,q) is available for all qεQ(m) from the processing used to obtain the MMSE receive beamforming weights.
In the preferred embodiment, adaptation of the power is done in a series of measured and quantized descent steps and ascent steps, to minimize the amount of control bits that need to be supported by the network. However, in an alternative embodiment, a node may use more bits of control information to signal for and quantize large steps.
Various alternative methods can be used to develop the local model for each node. The preferred embodiment's use of measured data (e.g. function values or gradients) to develop the local model valid in vicinity of the current parameter values, is only one approach; it can, however, be readily optimized within the node and network. The usual model of choice in prior art has been the quadratic model, but this was inadequate as elements of the functions are monotic. One alternative embodiment is to use a log-linear fractional model:
where βq is the achievable bit capacity as a function of the transmit gains π1(q); and to characterize the inequality
{circumflex over (β)}q(π1(q))≧β EQ. 35
with a linear half-subspace, and then solving the approximation problem with a simple low dimensional linear program.
Another alternative embodiment develops the local mode by matching function values and gradients between the current model and the actual function. And another develops the model as a solution to the least squares fit, evaluated over several points.
Because of the isolating effect of the transmit and receive weights the fact that the transmit weights for the other nodes in the network may change mitigates the effect on the local model for each node. Yet another alternative broadens the objective function to include the effect of other links in the network, viewing them as responding to some extent to the transmit values of the current link q. A finer embodiment reduces the cross-coupling effect by allowing only a subset of links to update at any one particular time, wherein the subset members are chosen as those which are more likely to be isolated from one another.
In the preferred embodiment, and as shown in the figures, Node 2 optimizes the receiver weights during the uplink (when sending) using a MMSE function; then measures the SINR over all paths k for a particular channel q, and informs the sending node 1 both of the measured capacity for channel q, that is, (D12(q)) and, if the measured capacity experienced for that channel is too high, to lower the power, or, if the measured capacity for that channel is too low, to increase the power, with the power increase or decrease being done by small, discrete increments. Node 2 then sets, for that channel, the transmit weights to the receive weights and repeats this sub-process for the downlink case. By successive, rapid iteration node 2 rapidly informs node 1 of the precise transmission power needed at node 1 to communicate over channel q with node 2.
This process is performed for every channel q which is active at node 2, until either the target capacity is attained, or the capacity cannot be improved further. It is also repeated at every node in the network, so node 1 will be telling node 2 whether node 2 must increase or decrease the power for node 2's transmissions to node 1 over channel q.
In an alternative embodiment, the network contains one or more network controllers, each of whom govern a subset of the network. The network controller initiates, monitors, and changes the target objective (in the preferred alternative embodiment, capacity) for the set of nodes it governs and communicates the current objective to those nodes and the rest of the network as necessary. (See
The network controller, once it has initialized the reciprocal set and objective continually monitors the network of nodes it governs, continually compares if the desired capacity has been reached, and for each node n, perform a fitting function. (See FIG. LE2) If a node n is compliant with the power constraints and capacity bound, then R1(n) should be reduced by a small amount; but if node n has both power constraints and capacity violations than R1(n) should be incremented for that node. These increments and decrements are preferably quantized to fixed small numbers. In an alternative embodiment of the invention the scalar and history of the increments and decrements are recorded to feed into experientially modified approximations, effectively embodying a real-world adaptation learning for each node.
One important consequence of this approach is that compliance with any network constraint or objective can be conveyed with a single bit, and increment or decrement with two bits, thereby reducing the control overhead to a minimum.
From the Butler Mode Forming element received signals are first passed through the frequency channel bank, then mapped to the FDMA channels. The received data on channel k will be passed through both the Multilink Receive weight adaptation algorithm and the Multilink diversity combining, Receive weights W(k) element, which in turn both feed into the Multilink LEGO gain adaptation algorithm and thus feed-back into the multilink diversity distribution element for outgoing transmissions. The Multilink Receive weight adaptation algorithm passes the adapted data from channel k over to the Multilink Diversity combining, Receive Weights W(k) element passes on the signal to both the circuits for the Equalization algorithm and the Delay/ITI/pilot-gating removal bank, that strips out the channel-coordinating information and passes the now combined signal to the symbol decoder bank to be turned into the information which had been sent out from the originating transceiver, the inverse process, generally, from the symbol encoding at the transmission end.
These Signal Encoding Operations are graphically displayed in
Pilot tone generation, summarily disclosed on
When a communication is transmitted, it will be received; and the MIMO reception is, like the transmission, adaptive. See
A MIMO transceiver contains and uses simultaneously a multiple of single RF feeds. A signal passes between the Butler Mode Forming element and a Band Pass Filter, or preselection, element, and then between the Band Pass Filter element and the Transceiver switch. If the signal is being transmitted, it goes through a Low Noise Amplifier element and then back into the Transceiver switch; if the signal is being received, it goes through a Phase Amplifier and back into the Transceiver switch. The signal passes between the Transceiver switch and the Frequency translator, and then back into the Transceiver switch.
In the Frequency translator (
Different multitone formats are used at different transceivers, thereby enabling ready distinction by and amongst the receivers of the transmitter frequency tone set. For fixed transceivers (BS or fixed SU), rectangular windows with cyclic prefixes and/or buffers are used; for mobile transceivers, non-rectangular windows and guard times are used. This provides the network with a capacity fall-back as the network environment and traffic dynamics vary. In the preferred embodiment the guard times are matched to the cyclic prefixes and buffers, the multitone QAM symbols are matched at all windows, and the different windows and capacity are used in different modes.
The multitone (multifrequency) transmission that occurs between every pair of nodes when they form a communications link exploits the multipath phenomena to achieve high QoS results. Each node, when it is acting as a receiver, optimizes the receive weights, using the MMSE technique. This goes directly against Varanesi's assessment that “de-correlative and even linear MMSE strategies are ill-advised for such channels because they either do not exist, and even if they do, they are plagued by large noise-enhancement factors”.
An alternative embodiment uses the Max SINR technique, and any combination of these and other industry-standard receiver optimization algorithms are feasible alternative implementations. Then the transmit weights for that node in its reply are optimized by making them proportional to the receive weights. Finally, the transmit gains (gain multipliers that multiply the transmit weights) are optimized according to a max-min capacity criterion for that node, such as the max-min sum of link capacities for that transceiver node at that particular time. An alternative embodiment includes as part of the network one or more network controllers that assist in tuning the local nodes' maximum capacity criterion to network constraints, e.g. by enforcing a balancing that reflects an intermediate nodes' current capacity which is lower than the local, originating node's current capacity.
The MIMO network model for the aggregate data transmitted between N1 “Set 1 nodes” {n1(1), . . . , n1(N1)}, □ receiving data over downlink time slots and transmitting data over uplink time slots, and N2 “Set 2 nodes” {n2(1), . . . , n2(N2)} receiving data over uplink time slots and transmitting data over downlink time slots, can be approximated by
x2(k,l)≈i2(k,l)+H21(k,l)s1(k,l) EQ. 36
(uplink network channel model)
x1(k,l)≈i1(k,l)+H12(k,l)s2(k,l) EQ. 37
(downlink network channel model)
within frequency-time channel (k,l) (e.g., tone k within OFDM symbol l) transmitted and received at uplink frequency f21(k) and time t21 (l) and downlink frequency f12(k) and time t12(l), where
where
In many applications, for example, many airborne and satellite communication networks, channel response vector a1(f,t;n2,n1) can be characterized by the observed (possibly time-varying) azimuth and elevation {θ1(t;n2,n1), φ1(f,t;n2, n1)} of node n2 observed at n1. In other applications, for example, many terrestrial communication systems, a1(f,t;n2,n1) can be characterized as a superposition of direct-path and near-field reflection path channel responses, e.g., due to scatterers in the vicinity of n1, such that each element of a1(f,t;n2,n1) can be modeled as a random process, possibly varying over time and frequency. Similar properties hold for a2(f,t;n1,n2).
In either case, a1(f,t;n2,n1) and a2(f,t;n1,n2) can be substantively frequency invariant over significant breadths of frequency, e.g., bandwidths commensurate with frequency channelization used in 2G and 2.5 G communication systems. Similarly, a1(f,t;n2,n1) and a2(f,t;n1,n2) can be substantively time invariant over significant time durations, e.g., large numbers of OFDM symbols or TDMA time frames. In these cases, the most significant frequency and time variation is induced by the observed timing and carrier offset on each link.
In many networks of practical interest, e.g., TDD networks, the transmit and receive frequencies are identical (f21(k)=f12(k)=f(k)) and the transmit and receive time slots are separated by short time intervals (t21(l)=t12(l)+Δ21≈t(l)), and H21(k,l) and H21 (k,l) become substantively reciprocal, such that the subarrays comprising H21(k,l) and H21 (k,l) satisfy H21(k, l; n2, n1)≈δ21(k, l; n1, n2) HT12(k, l; n1,n2), where δ21(k, l; n1, n2) is a unit-magnitude, generally nonreciprocal scalar.
If the observed timing offsets, carrier offsets, and phase offsets are also equalized, such that λ21(n2,n1)≈π12(n1, n2), τ21(n2,n1)≈τ12(n1, n2), and ν21(n1,n2)≈ν12(n2, n1), for example, by synchronizing each node to an external, universal time and frequency standard such as Global Position System Universal Time Coordinates (GPS UTC), then ε21(k, l; n1, n2)≈1 can be obtained and the network channel response becomes truly reciprocal, H21(k,l)≈HT12(k,l). However, this more stringent level of reciprocity is not required to obtain the primary benefit of the invention.
In order to obtain substantive reciprocity, each node in the network must possess means for compensating local differences between transmit and reception paths. Methods for accomplishing this using probe antennas are described in Agee, et. al. (U.S. patent application Ser. No. 08/804,619, referenced above). A noteworthy advantage of this invention is that substantive reciprocity can be obtained using only local transmit/receive compensation means.
The channel model described above is extendable to applications where the internode channel responses possess substantive multipath, such that H21(k, l; n2, n1) and H12(k, l;n2,n1) have rank greater than unity. This channel response can also be made substantively reciprocal, such that the primary benefit of the invention can be obtained here.
The preferred embodiment uses a substantively null-steering network wherein each node transmits baseband data (complex symbols provided by a multirate codec) through the multiplicity of reciprocal linear matrix operations prior to transmission into the antenna array during transmit operations, and after reception by the antenna array during receive operations, in a manner that physically separates messages intended for separate recipients. This is accomplished by
(1) forming uplink and downlink transmit signals using the matrix formula
s1(k,l;n1)=G1(k,l;n1)d1(k,l;n1) EQ. 40
s2(k,l;n1)=G2(k,l;n2)d2(k,l;n2)
where
d1(k,l;n1)=[d1(k,l;n2(1),n1) . . . d1(k,l;n2(N2),n1)]T
represents the vector of complex data symbols transmitted from node n1 and intended for each of nodes {n2 (q)}, respectively, within uplink channel (k,l);
d2(k,l;n2)=[d2(k,l;n1(1),n2) . . . d2(k,l;n1(N1),n2)]T
represents the vector of complex data symbols transmitted from node n2 and intended for each of nodes {n1(q)}, respectively, within downlink channel (k,l);
G1(k,l;;n1)=[g1(k,l;n2(1),n1) . . . g1(k,l;n2(N2),n1)]
represents the complex distribution weights used to redundantly distribute symbol vector d1(k,l; n1) onto each diversity channel employed at node n1 within uplink channel (k,l); and
G2(k,l;;n2)=[g2(k,l;n1(1),n2) . . . g2 (k,l;n1(N1),n2)]
represents the complex distribution weights used to redundantly distribute symbol vector d2 (k,l;; n2) onto each diversity channel employed at node n2 within downlink channel (k,l);
(2) reconstructing the data intended for each receive node using the matrix formula
y1(k,l;n1)=WH1(k,l;n1)x1(k,l;n1) EQ. 41
y2(k,l;n2)=WH2(k,l;n2)x2(k,l;n2)
where ( )H denotes the conjugate-transpose (Hermitian transpose) operation, and where
y1(k,l;n1)=[y1(k,l;n2(1),n1) . . . y1(k,l;n2(N2),n1)]T
represents the vector of complex data symbols intended for node n1 and transmitted from each of nodes {n2 (q)}, respectively, within downlink channel (k,l);
y2(k,l;n2)=[y2(k,l;n1(1),n2) . . . y2(k,l;n1(N1),n2)]T
represents the vector of complex data symbols intended for node n2 and transmitted from each of nodes {n1(p)}, respectively, within uplink channel (k,l);
W1(k,l;n1)=[w1(k,l;n2(1),n1) . . . w1(k,l;n2(N2),n1)]
represents the complex combiner weights used at node n1 to recover symbol symbols {d1(k, l; n2(q),n1)} intended for node n1 and transmitted from nodes {n2(q)} within uplink channel (k,l); and
W2(k,l;n2)=[w2(k,l;n1(1),n2) . . . w2(k,l;n1(N1),n2)]
represents the complex combiner weights used at node n2 to recover symbol symbols {d2(k, l; n1(p),n2)} intended for node n2 and transmitted from nodes {n1(p)} within uplink channel (k,l).
(3) developing combiner weights that {w1(k,l;n2, n1)} and {w2 (k,l;n1, n2)} that substantively null data intended for recipients during the symbol recovery operation, such that for n1≠n2:
(4) developing distribution weights {g1(k,l;n2, n1)} and {g2(k,l;n1, n2)} that perform equivalent substantive nulling operations during transmit signal formation operations;
(5) scaling distribution weights to optimize network capacity and/or power criteria, as appropriate for the specific node topology and application addressed by the network;
(6) removing residual timing and carrier offset remaining after recovery of the intended network data symbols; and
(7) encoding data onto symbol vectors based on the end-to-end SINR obtainable between each transmit and intended recipient node, and decoding that data after symbol recovery operations, using channel coding and decoding methods develop in prior art.
In the preferred embodiment, OFDM modulation formats is used to instantiate the invention, and substantively similar distribution and combining weights are computed and applied over as broad a range of tones (frequency channels k) and OFDM symbols (time slots l) as is practical. The range of practical use is determined by the frequency selectivity (delay spread) and time selectivity (Doppler spread) of the communications channel, which determines the degree of invariance of the channel response vectors a1 and a2 on (k,l); the dynamics of interference i1 and i2; latency requirements of the communications network; and dimensionality of linear combiners used at each node in the network, which determine the number of frequency-time channels needed to determine reliable substantively null-steering distribution and combining weights.
In the preferred embodiment, substantively nulling combiner weights are formed using an FFT-based least-squares algorithms that adapt {w1(k,l;n2, n1)} and {w2(k,l;n1, n2)} to values that minimize the mean-square error (MSE) between the combiner output data and a known segment of transmitted pilot data. Operations used to implement this technique during receive and transmit operations are shown in
where symbol index l is referenced to the start of the adaptation frame, and where
The “pseudodelays” δ1(n1) and δ2(n2) can be unique to each transmit node (in small networks), or provisioned at the beginning of communication with any given recipient node (in which case each will be a function of n1 and n2). In either case, the minimum spacing between any pseudodelays used to communicate with a given recipient node should be larger than the maximum expected timing offset observed at that recipient node. This spacing should also be an integer multiple of 1/K, where K is the number of tones used in a single FFT-based LS algorithm. If K is not large enough to provide a sufficiency of pseudodelays, additional OFDM symbols can be used for transmission of pilot symbols, either lengthening the effective value of K, or reducing the maximum number of originating nodes transmitting pilot symbols over the same OFDM symbol (for example, the recipient node can direct 4 originators to transmit their pilot symbols over the first OFDM symbol in each adaptation frame, and 4 other originators to transmit their pilot symbols over the next OFDM symbol, allowing the recipient node to construct combiner weights for 8 originators). In the preferred embodiment, K should also be large enough to allow effective combiner weights to be constructed from the pilot symbols alone. The remaining information-bearing symbols in the adaptation frame are then given by
d1(k,l;n2,n1)=p1(k;n2,n1)d01(k,l;n2,n1) EQ. 46
d2(k,l;n1,n2)=p2(k;n1,n2)d02(k,l;n1,n2) EQ. 47
where d01(k,l;n2, n1) and d02(k,l;n1, n2) are the uplink and downlink data symbols provided by prior encoding, encryption, symbol randomization, and channel preemphasis stages.
Preferably, the adaptation frame is tied to the TDD frame, such that the TDD frame comprises an integer number of adaptation frames transmitted in one link direction, followed by an integer number of adaptation frames transmitted in the reverse link direction. However, the OFDM symbols in the adaptation frame may be interleaved to some degree or any degree. The pilot data may also be allowed to pseudorandomly vary between adaptation frames, providing an additional layer of “physical layer” encryption in secure communication networks.
At the recipient node, the pseudorandom pilot components are first removed from the received data by multiplying each tone and symbol by the pseudorandom components of the pilot signals
x01(k,l;n1)=c02(k;n1)x1(k,l;n1) EQ. 53
x02(k,l;n2)=c01(k;n2)x2(k,l;n2) EQ. 48
where c02(k;n1)=[p02(k)p12(k;n1)]* and c01(k;n2)=[p01(k)p21(k;n2)]* are the derandomizing code sequences.
This operation transforms each pilot symbol authorized and intended for the recipient node into a complex sinusoid with a slope proportional to the sum of the pseudodelay used during the pilot generation procedure, and the actual observed timing offset for that link (observed pseudodelay). (See
The FFT-based LS algorithm is shown in
A valley (or peak) finding algorithm is then used to detect each of these valleys (or peaks), estimate the location of the observed pseudodelays to sub-lag accuracy, and determine additional ancillary statistics such as combiner output SINR, input SINR, etc., that are useful to subsequent processing steps (e.g., LEGO). Depending on the system application, either the Q lowest valleys (highest peaks), or all valleys below a designated MSE threshold (peaks above a designated SINR threshold) are selected, and spatially whitened weights U are interpolated from weights near the valleys (peaks). The whitened combiner weights U are then used to calculate both unwhitened combiner weights W=R−1U, used in subsequent data recovery operations, and to estimate the received channel aperture matrix A=RHU, to facilitate ancillary signal quality measurements and fast network entry in future adaptation frames. Lastly, the estimated and optimized pseudodelay vector δ* is used to generate c1(k)=exp{−j2πδ*k} (conjugate of {p11(k;n1)} during uplink receive operations, and {p22(k;n2)} during downlink receive operations), which is then used to remove the residual observed pseudodelay from the information bearing symbols. (See
In an alternate embodiment, the pseudodelay estimation is refined using a Gauss-Newton recursion using the approximation
exp{−j2π(k−k0)/PK}≈1−j2π
(k−k0)/PK
This algorithm first estimates , providing an initial sublag estimate of pseudodelay, before estimating the lag position to further accuracy. The resultant algorithm can reduce the padding factor P, and reduces interpolation errors in the receive combination weights. However, it requires estimation of an additional IFFT using a modified FFT window, and is therefore not preferred in applications where DSP complexity is of overriding importance.
The optimized combiner weights are substantively null-steering, in that the combiner weights associated with each originating signal will (notionally, in absence of multipath) form a composite antenna pattern that steers nulls in the direction of all other time-and-frequency coincident signals (signals transmitting on the same time slot and frequency channel) impinging on the array. However, the weights will also (notionally, in absence of multipath) form a beam in the direction of the originating signal, further improving performance of the overall network. In the presence of multipath, a clear gain pattern of this sort may not necessarily form; however, the effect of this processing will be the same, and is typically be improved due the added diversity provided by multipath.
In additional alternate embodiments, the combiner weights can be further refined by exploiting known or added structure of the information bearing symbols using blind property-restoral algorithms. Algorithms of this sort are described in Agee (U.S. Pat. No. 6,118,276) and Agee, et. al., (U.S. patent application Ser. No. 08/804,619, referenced above) as well as other disclosures in the public domain. These alternate embodiments can reduce the size of K, or allow the airlink to be extended into more complex systems where the linear combiner dimensionalities are too large to allow computation of effective weights given the value of K employed in an existing system.
The resultant network has several useful attributes over prior art. It is computationally efficient, especially for nodes receiving data from large numbers of originating nodes, since the complex operations employed in the FFT-LS algorithm can be amortized over multiple links. It is also rapidly convergent, allowing computation of 4-element diversity combiner weights to attain nearly the maximum SINR obtainable by the combiner using 8-to-16 pilot data tones. It automatically detects and reconstructs data from nodes that have been authorized to communicate with the network, or with recipient nodes within the network, and rejects nodes that are not so authorized, allowing the network to adjust and control its topology and information flow at the physical layer, and providing an important level of security by rejecting signals that do not possess appropriate network or recipient pilots. It also provides an additional level of data scrambling to prevent occurrence of correlated interlink symbol streams that can cause severe misadjustment in conventional linear combiner adaptation algorithms.
In reciprocal channels, the linear combiner weights provided during receive operations can be used to simply construct linear distribution weights during subsequent transmit operations, by setting distribution weight g1(k,l;n2, n1) proportional to w*1(k,l;n2, n1) during uplink transmit operations, and g2(k,l;n1, n2) proportional to w*2(k,l;n1,n2) during downlink transmit operations. The transmit weights will be substantively nulling in this system, allowing each node to form frequency and time coincident two-way links to every node in its field of view, with which it is authorized (through establishment of link set and transfer of network/recipient node information) to communicate.
Among other advantages, this capability allows nodes to independently adjust transmit power directed to other nodes in the network, for example, to optimize capacity achievable at that node given the total power available to it, or to minimize power emitted into the network by that node given an aggregate power requirement. This capability also allows the node to adjust its contribution to the overall network, for example, to maximize the total aggregate (max-sum) capacity of the network, or to minimize network power subject to a network-level capacity constraint. In addition, this capability can allow the node to provide two-way communication to authorized nodes, or in defined subnets, in the presence of other nodes or subnets that it is not authorized to communicate with, for example, adjacent cells in CMRS networks, and adjacent (even interpenetrating), and virtual private nets. In wireless LAN's and MAN's.
This capability is illustrated in
The pseudodelay indicator functions (provided as a function of SINR, i.e., with peaks at observed pseudodelays) are shown for each of the nodes. Indicator functions generated at nodes B, C, E, and F have two strong peaks, corresponding to pseudodelays used at their connecting nodes, plus a 10 microsecond time-of-flight delay (assuming all nodes are synchronized to GPS UTC). In addition, nodes A and D have a third peak at their respective delays, plus a 20 microsecond time-of-flight delay. The pseudodelays are minimally separated by 25 microseconds for each of the originating nodes (12.5 microsecond minimal separation between all nodes), which is easily wide enough to allow peaks from different originators to be discerned. In addition, the peak values (near 20 dB SINR for all links except the A-to-D link) are detectable with a 0 dB threshold. As Figure ## shows, the receive and transmit weights form beam and null patterns that allow independent links to be formed between authorized nodes, and that allow unauthorized signals to be screened at the points or reception and transmission.
The aperture estimates A will (also notionally, in absence of multipath) form beams in the direction of the originating node; however; they will ignore all other nodes in the network. For this reason, they cannot be used in general to sustain independent links. However, the aperture estimates can be used to allow rapid reentry into the network, for example, in packet data systems where users may quickly begin and end signal transmissions over brief time periods (e.g., such that the channel response has not changed substantively between transmissions). The aperture estimates can also be combined with combiner/distribution weights to form rapid nulls again other links or nodes in the network.
The primary application area for the fully adaptive MIMO arrays of the preferred embodiment will be below 10 GHz, where the abilities to achieve non-LOS and to exploit multipath are still possible, and where pathloss, weather effects, and channel dynamics can be handled by adaptive arrays. The preferred embodiment's MIMO network will provide a strong advantage over conventional MIMO links, by not requiring antenna separation of 10 wavelengths to provide effective capacity gain. The present state of the art considers 10 wavelengths to be the rule of thumb for the distance between antennas that provides spatially independent antenna feeds due to disparate multipath at each antenna. This rule has greatest applicability in worst-case mobile environments subject to Rayleigh fading, i.e., where the (typically much stronger) direct path is obscured, and propagation occurs over many equal-power reflection paths.
The MIMO network of the preferred embodiment, however, exploits route diversity due to reception of signals from widely separated nodes, and does not need multipath to provide the capacity gains cited for MIMO links in the present state of the art. This enables the preferred embodiment to employ antennas with much smaller separation, e.g., circular arrays with half-to-full wavelength diameter, to provide effective capacity or QoS gains. The preferred embodiment's exploitation of multipath can further improve performance, by providing additional differences between gain and phase induced at each antenna in the array. In this regards, a smaller aperture is also better, as it reduces frequency selectivity across individual frequency channels. Polarization diversity can also be employed between antennas with arbitrary spacing (e.g., in “zero aperture” arrays), as well as “gain diversity” if the antennas have distinct gain patterns.
The MIMO network of the preferred embodiment has application in the 10-100 GHz region (for example, LMDS bands around 25-35 GHz where mesh networks are of increasing interest), even though these networks are likely to employ nonadaptive directional antennas, or partially adaptive antennas, e.g., arrays in focal plane of directional dish antennas, that direct high gain or “pencil” beams at other ends of the link, rather than fully adaptive beam-and-null steering networks. This is due to small form factor of such antennas, as well as pathloss, atmospheric absorption, and weather effects prevalent above the 10 GHz band.
The next preference is that for each channel that is dynamically established, the uplink and downlink share a common frequency (that is, the transmission and reception are on the same frequency). This enables the establishment and exploitation of channel reciprocity (CR) between pairs of nodes, the sharing of antennae and diversity channels in transmit and receive operations in particular nodes, and other network advantages. The network advantages include the use of ad hoc, single-frequency networks in bursty (data-intensive) networks, such as packet-switched networks, random-access networks, or at the network “edges” (where the SINR level threatens to overcome the network capacity). This also allows the establishment of a two-layer time-division duplex schema in persistent networks or channels (e.g. ones that are circuit-switched, or perform connectionless datagram backhaul functions), where there is an equal duty cycle in both directions. An alternative embodiment will permit asymmetric duty cycles, and yet a third configurable balancing of duty cycles. Additionally, in the multitone case of dual-frequency approach (uplink and downlink using distinct frequencies), the network uses a frequency division duplex (FDD) protocol, preferably in combination with channel-based transmission and reception weights.
The network advantage to the preferred embodiment is that, instead of the network serving as a Procrustean bed to which the communications links must be fitted out of the combination of its environmental SINR, established protocols, and channel approach, each communications link can use the environmental SINR, protocols, and channel approach to dynamically adapt the network's functioning to maximize capacity and minimize power consumption.
The second preference is that the network uses and exploits diversity frequency transmission and reception at all nodes in the network. This particular aspect of the preferred embodiment carries the complexity and hardware cost of requiring that each node incorporate spatially separated and shared receive/transmit antennae, although the separation need only be measured in tens of lambdas of the lowest frequency (longest wavelength) used by that particular node. This pragmatically can create a situation where BS nodes are equipped with larger antennae which are spatially separated by feet or meters, and thus use far lower frequencies for ‘backhaul’ or BS to BS transmissions; this also carries, however, the advantage that SU nodes lacking such spatial separation will be intrinsically deaf to such frequencies. (Care must be taken to consider harmonics between BS backbone frequencies and SU channel frequencies.)
Optionally, in an enhancement to the preferred embodiment, the network would include and make use of frequency polarization, spectral diversity, or any combination thereof, at any subset (including a proper subset) of the network's nodes to provide further coding and differentiation potential. And in another, further enhancement, the network would employ Butler RF networks to provide common RF front-end and scalable and expandable transceiver DSP backends in peer-to-peer network implementations.
The third preference is that the nodes include a multitone QAM encoder, whereby individual tones would be multiplied by Quadrature-Amplitude-Modulated symbols to further differentiate the signals between nodes, even those using the same frequencies. Alternative QAM approaches would include PSK, π/4 QPSK, and π/4-DQPSK symbols to increase the variation potentialities. These symbols would be generated using Trellis-Coded-Modulation (TM) encoding over individual frequency channels and would include several-to-one multitone symbols. One alternative embodiment would use Reed-Solomon codes and direct mapping to symbols; other alternatives would use Turbo encoding, either at the baseband or as part of the TM, or any combination thereof. To aid the Viterbi decoding at the receiver the ‘tail-biting approach would be used at the edge of the symbol blocks. To assist the maximum capacity solution for each frequency channel, the network would use variable information bits per frequency channel rather than a fixed set of information bits per frequency channel, in a method analogous to the Digital MultiTone, Digital Signal Loss (DMT DSL) approach, trading the need for information density encoding as part of the signal overhead for the need for all channels to be constrained to the minimum guaranteed capacity of any environmentally or hardware constrained channel, to avoid pathloss for the most tightly constrained link or channel. However, rather than insist upon this approach, the network would include the capability to shift to a constant bits per frequency channel approach with appropriate LEGO power management, to enable and support the minimum-power solution for the network when either power or capacity constraints determine this is preferable.
The fourth preference is that the network adds pseudorandom modulation to the symbols after encoding. This is to eliminate the need to increase the signaling overhead by runs of correlated symbols, as it aids in the receive adaptation algorithms, provides discrete link encryption, thereby greatly increasing both channel and network security, and enables pilot-gated fast acquisition and timing recovery algorithms. An extension to the pseudorandom modulation is the analysis and elimination of certain detectable features by the network in one alternate embodiment. A distinct extension is the embedding of invariance for exploiting broader modulation, using gated SCORE. And a third extension combines the two extensions just described.
The fifth preference is the addition of an error detection syndrome, or CRC block to transmissions to detect bit errors, which would enable the initiation of a retransmission request at the end of a packet's reception when an error is signaled.
The sixth preference is using a computationally efficient and fast-converging receive weight algorithm (CE&FC RWA) to reduce the computational and hardware overhead for each channel's transmission and node. Variations of such CE&FC RWA that would be used include any one, subcombination, or combination, of the following: Least-Squares Like (LS), which are also known as matrix-inversion; Block-Update implementations (on a per-packet basis) that amortize matrix operations over multiple data snapshots (using tones and/or multitone symbols); and recursive single-snapshot algorithms. Furthermore, the preferred embodiment may use one, more than one, or all of the following for the same purposes: calculation of autocorrelation statistics in voltage domain (e.g. using QRD, MGSO) to minimize the complexity and increase the accuracy of the weight-update operation; multiport adaptation (simultaneous processing of multiple co-channel links) on each frequency channel to amortize autocorrelation operations over multiple users (more at BS than SU nodes); or single-step, single-port adaptations (more at SUs than BSs). Depending on the network constancy and dynamic state, or static constancy, the network may vary between uncalibrated techniques which are not dependent upon knowledge or calculation of channel information (e.g. the emitter location, or the elevation/azimuth separating transmitter and receiver), non-blind and blind weight adaptation techniques such as pilot-based initial weight acquisition signaling, blind and/or pilot-aided decision-direction weighting in persistent links, and blind embedded-invariance techniques such as gated SCORE, in an alternative embodiment. For pilot-aided and gated SCORE techniques the network would preferentially use the computationally efficient mechanization of cross-correlation operations employing fast transform (and in a specific embodiment, FFT) methods. In yet other alternative embodiments the network may use combined channel sounding, channel-based weight estimation, or any combination of the foregoing.
The seventh preference is using post-combining in-channel tone equalization to remove timing and carrier offset. This could include multiplication by constant modulus weights, as the first preference, to remove timing and/or delay offsets; alternatively, it could include low-complexity intertone filtering to remove carrier offset and Doppler errors; and, of course, a combination of both could be employed depending on the environmental and hardware complexity needs, constraints, and costs.
The eighth, and most important preference for the preferred embodiment is that each node of the network be capable of employing and employ retrodirective transmission and reception modulation, wherein the transmit gains are set proportional to the actually experienced reception weights for the frequencies used. For single frequency links, this exploits their potential reciprocity (especially for TDD or ad-hoc networks). When a TDD approach is used each data frame is encapsulated in smaller guard frames, and the entire transmission occupies a smaller portion of the available bandwidth to similarly encapsulate it in the available bandwidth. The signal for a one-way frame duration is further broken down to incorporate a guard time, a data symbol, an encapsulating cyclic prefix, a control symbol, a cyclic prefix separation the control symbol from the acquisition symbol, and a final encapsulating cyclic prefix. The frequency channels that occupy the bandwidth carry bearer data fragments over fractional subfragments. One embodiment for low-mobility, fixed or portable TDD uses a 120B Bearer Data Fragment which is comprised of eight 15B subfragments, 8 differentiating and coordinating multitone symbols, and of 5.75 MHz available only 4.26 MHz bandwidth, said bandwidth being divided into 13 frequency channels, each with 320 kHz, to provide 2 fragments per frame per link, or 6.24 Mbytes each frame, or 4.608 Mbps as one channel (Channel 0) is reserved for fragment resends, thereby providing the equivalent in wireless transmission of 3 land-based T1 lines with, thanks to the reservation and resend provision, a 10−4 BER. This embodiment further uses for each 15B subfragment a MAC header providing 2B CRC and 13B MAC data, providing 52 MAC channels and at full duplex 10.4 kbps per channel. The acquisition symbols have 30B pilot or synchronization data per 320 kHz frequency channel, 32 to 64 pilot tones per channel, and thereby provide fast acquisition for up to 32 Degrees of Freedom; and if the area is sparsely populated or for other reasons (downtime, occupied by other transmissions) less than 17 Degrees of Freedom were needed, the excess DOF could be reused and reprovisioned to enable dynamic channels and thereby further increase the local flexibility.
The high-mobility TDD link replaces the cyclic prefixes with nulls on the uplink and CP on the downlink, halves the number of tones and doubles the separation of tones (from 426 to 213 tones, from 10 kHz to 20 kHz separation), and provides half the DOF, but doubles the amount of overlap that can be tolerated for the same QOS.
In the preferred embodiment for fixed, portable, and low-mobility links, the tone layout divides the 4.26 MHz into 426 ‘bins’, each of 10 kHz separation; these are then shared such that thirteen channels, each with 32 tones covering 320 kHz, from Channel 0 to Channel 12 are formed, with each channel further carrying of the 32 tones a network-information-bearing tone at the bottom and top of the channel (T0 and T31) that encapsulate the content-carrying tones T1 through T30. Each channel is modulated by a 32-tone pilot to facilitate the acquisition and fine time synchronization. The 10 kHz tone separation controls reasonable levels of time selective Multipath (+/−100 MHz), with a cyclic buffer being added at channel edges. The network can, should environmental or network conditions suggest, ‘step down’ the overall frequency spread to 160 kHz BW without affecting the fundamental stability of the traffic algorithms or network. (See
However, for high-mobility TDD links the number of bins, pilot size, and information-bearing tones per channel are halved, while the tone separation is doubled. This will permit high levels of Doppler shift (+/−5 kHz) without sacrificing QoS or content integrity; and again, the network can step down the overall frequency spread and thus the bandwidth per bin can be halved (from 320 kHz to 160 kHz) without affecting the fundamental stability of the traffic algorithms or network. (See
Preferentially transmit gains are set proportionally to the conjugate of the receive weights for that particular node and channel. An alternative approach uses channel-based retrodirective transmit gains (more for SU than BS); a second alternative uses channel-based directive (beam-pointing) transmit gains (more for BS than SU); a third applies retrodirection to in-channel preemphasis gains; and any combination of these alternative approaches may be employed. For any such single-frequency link the transmitting node breaks periodically (in one particular alternative embodiment every 5 msec) to collect ACKs, NACKs, or RTSs, that is, to monitor the link performance as perceived by the receiving node. This approach, though it provides all the capacity in a particular link to a user as needed, is very compatible with small, stationary networks but less compatible with LEGO network management due to the effects of nonstationary network fields.
The ninth preference is that the network employs Locally-Enabled Network Optimization (LEGO) to manage the transmit power for each node (BS and SU) operating, dynamically. This requires that relatively complex computational operations (e.g. receive weight and transmit gain calculations, multitone, QAM, TCM, and the above-mentioned signal/symbol/weight/frequency calculations) be carried out autonomously at each node in the network, rather than limited to one class of nodes. This further requires that as part of the network overhead simple, network-level control parameters be passed to, or shared by (for certain time intervals, though such may either be hard-set invariances in the hardware, subject to change signal, or network-alterable) all nodes in the network. Additionally, each node would implement its power-management algorithm to minimize transmit power and manage its links, thereby indirectly optimizing performance over the entire network. An alternative embodiment would effect network-level optimization; and a third would combine node-driven local determined optimization with network-level optimization.
Although the preferred embodiment uses an algorithm that presumes that power capacity will vary over the network, and that establishes local maxima by favoring capacity maximization for the power constraint at each particular node in the network (i.e. a goal driven minimization algorithm), various alternative LEGO algorithms could be employed. For example, if power shortfalls or constraints on any part of the network are anticipated, then a capacity maximization subject to that power constraint algorithm could be used. A third alternative, presuming that the network capacity (as opposed to the power) is the guiding constraint, sets the power minimization subject to the capacity attainment to the limit possible over the entire network. And a fourth, which is better, sets the power minimization at each particular node in the network subject to the capacity constraint at that particular node.
The preferred embodiment incorporates into each node a multitone QAM decoder, with a soft-optimized, Viterbi algorithm (SOVA) embodied in the decoder, such that the network can effect changes in the decoder at its nodes by a software or information transmission that re-sets the hardware (EEPROM, FPGA, PAL, or other semiconductor chip) and software at that node for the new decoding scheme. An alternative embodiment with lesser cost and complexity at each node, but lesser flexibility, is to use hard-optimized, Viterbi or Reed-Solomon, decoders at each node in the network. A third alternative is to combine both SOVA and HOVA decoders in the network and establish hierarchies wherein the more flexible stations moderate as needed to communicate with their less flexible but simple contacts.
The preferred embodiment of the present form of the invention also incorporates synchronization means for its communications, which encompass timing estimation, carrier estimation, and synchronization operations as part of the network communication and control methodologies. The preferred synchronization is to a single, universal, and commonly observable timing signal such as that used in GPS operations, and occurs as part of the carrier signal (also known as ‘GPS Sync’). An alternative embodiment would use synchronization to a timing, carrier, or mutual offset which would be observed at the transmission or master node during the reception process, wherein the slaved receiver synchronization would introduce a x2 delay and carrier error at the slaved transmitter, to avoid interference with the master transmission. Another alternative embodiment would use precompensation (in timing, carrier advancement, or both), to equalize any timing or carrier offset observed at both ends of the link (a means to synchronize the slaved node's transmission). Combinations of universal, offset, or precompensation synchronization methods would be yet further alternative embodiments.
Synchronization would be performed by including in the transmission dedicated multitone signals (such forming part of the set of QAM symbols used by the preferred embodiment), or by using dedicated tones in each multitone symbol, or most preferentially, by combinations of dedicated tones and slots to maximize the synchronization possible for the minimal transmission density. Coarse synchronization would be performed prior to the multitone demodulation, using the envelope features of the waveform. Fine synchronization would be performed after multitone demodulation, using dedicated QAM synchronization symbols and tones. For embodiments using universal observed timing through GPS synchronization, or using slaved transmission synchronization, these would be performed using control or MAC channels. An alternative embodiment would bypass the synchronization operation entirely by using GPS-based timing and carrier acquisition methods. A separate alternative embodiment would use blind, data-based synchronization methods, minimizing the use of specific synchronization data.
In the preferred embodiment Transmit/Receive (T/R) compensation means are employed to remove nonreciprocal channels after shared transmit or receive operations. These would be employed intermittently on an ‘as-needed’ basis through transmission of specialized T/R compensation packets to initiate the compensation processing. An alternative embodiment would use dedicated T/R compensation channels to initiate the compensation processing. And the network would employ loops back of the received signal data to provide the initial transmitter with the T/R channel differences.
The preferred embodiment further includes methods for datagram network instantiations, particularly applicable for conditions such as edge networks (e.g. where the wireless electromagnetic communications network is connecting to the Internet) and entirely interior networks (e.g. the ‘backhaul’, dedicated, data-heavy, and often fiber-optic networks of other carriers). These enable the transmission of data in discrete datagrams, or fragments of datagrams, over multiple routes, such as neighboring nodes, according to the availability of transmission channel capacities. The recipient nodes would then reconstitute the original data stream from the received and re-ordered datagrams or datagram fragments. The preferred embodiment mechanizes the process by incorporating, or enabling, both TCP/IP and FTP protocols, and further uses fragment-level CRC's, error detection, and retransmission protocols to provide Zero-error, Uncommitted Bit-Rate (ZE-UBR) services. By using reservation protocols such as VolP RSVP common to the industry, the preferred embodiment can also provide Committed Bit-Rate (CBR) service.
The preferred embodiment also incorporates means for resolving scheduling and capacity problems, preferentially the soft-contention and Demand-Assigned, Multiple-Access (DAMA) scheduling means. These would primarily be employed at network edges, though they also can serve at ‘bursty’ edge networks or handle ‘unconcentrated’ data streams. The soft contention means minimize the effects of data collisions and the latency due to retransmissions and/or backoff network effects; the DAMA scheduling is principally employed over longer sessions to maximize the network efficiency.
The topology of the wireless electromagnetic communications network affects the local details of implementation of the preferred embodiment, as different constraints and needs dictate how the best adaptation occurs. For small network embodiments where most, if not all of the nodes are in a common field of view, it is not important whether the network be in a star, ring, bus, or mesh topology. Under these conditions the preferred embodiment matches each transceiver's Degrees of Freedom (DOF) to the nodes in the possible link directions and equalizes them to provide node-equivalent uplink and downlink capacity. An alternative embodiment may also be used, depending on network traffic or user payment/preferences, wherein asymmetric transceiver assignments reflect the desired capacity weighting. After the DOF matching is completed, each node adapts the Receive Weights to form a hard (max-SINR, null-steered) or soft (max SINR) solution for multipath resolution for transmissions to that node. Then explicit interference whitening for in-network nodes, or implicit data whitening for soft nulling of out-of-network interferers are employed for conditions, e.g. as in Part 15 applications. Finally, retrodirective transmit gains (whitened or unwhitened as above) are used during subsequent transmission operations during a channel communication. In an alternative embodiments, the Receive Weights are directive, whitened, channel-based, or a combination thereof.
For large network embodiments the fundamental conditions are different and thus a different implementation and adaptation strategy is usually required. These include mesh extensions of star, ring, and bus networks (see
A third possible topology and concomitant operating conditions occurs when the network is cellular, or has overlapping subordinate or coordinating networks. Under these conditions, which are particularly likely to occur under competition, not all the nodes will be connected to the same wireless electromagnetic communications network. A greater potential for signal interference which is beyond the network's control results and the preferred embodiment adapts to this constraint. Furthermore, there may be some minimal transmissions of control, network health, or network OAMP data through a disparate infrastructure (wired or wireless), or even a high-rate connection through a wired infrastructure in an alternative embodiment.
When these conditions occur and networks are in view of each other, they can experience signal or physical overlapping; in many situations, such as urban areas, there may be heavy interpenetration. In principle there will be physical separation of the disparate networks' nodes, at least as to their geographic identity (two mobile phones generally do not share the exact, same physical location and continue to work when so crushed together); in practice, however, all the networks' nodes share the same physical layer of the electromagnetic spectrum and the real world geography and hardware. What ensures the continued separation of the networks is and enforced separation at the non-physical, information and communication layer. Alternative embodiments may allow low-rate communication, or means for limited, or even allowable full inter-network communication, depending on the differing networks' contract agreement as to communications and provisioning sharing.
Under these conditions the nodes in the preferred embodiment direct nulls (hard or soft) at all observed Transmission nodes in other networks, to minimize the interference from and with the other network's signals. This same approach may be used, in an alternative embodiment, to enforce a ‘lock out’ of unauthorized nodes in a secure network. The nulls are further enabled using network-wide scrambling, gating, or encryption means as described elsewhere, to differentiate the two networks' internal signals.
An alternative embodiment incorporates one or more broadcast modes from network master controllers, that would enforce common timing standards and provide broadcast, i.e. network common information, without requiring two-way bandwidth for such effort.
Advantages: LEGO
Among the advantages of the preferred embodiment's solution of the local optimizations to obtain global optimization are the following: (1) the working target capacity objective for any given set of nodes may be rapidly reached by iterating from an initial approximation to an acceptably-constrained solution with many fewer iterations overall; (2) the power levels that solve the local target capacity objective minimize the transmitted energy at the local node, thereby minimizing the co-channel interference to other uses in the network; and (3) the quantities needed for the local solution only require local information to solve, thereby reducing substantially the ratio between ‘control’ and ‘content’ information, thus further enhancing the overall capacity of the network.
Additional advantages of the preferred embodiment's LEGO approach are (1) that it can be solved at each node using a very simple but powerful approximation technique that converges rapidly to the correct solution; (2) the power levels, at each respective node, that solve Eq. 3 actually minimize the transmitted energy at each respective node, hence (3) minimizing co-channel interference to other users (nodes) in the network while achieving the targeted capacity rates; and (4) most of the quantities required for the optimization only require local information.
Further advantages of the preferred embodiment include the lack of any need for estimating any channel matrices., and substantial lessening of detailed and fine calculation and recalculation of both (1) the initial SINR ratios and (2) the effect on each node of changing the power usage of any other node in the network. This latter has a secondary effect of reducing the amount of ‘control’ information that needs to be sent across the network, and reduces the amount of work or complexity that has to be managed by the network controller.
LEGO Effect on the MIMO Network
By solving the optimization at the local level for each node of the network, the problem of network optimization becomes a hierarchical one, where the overall problem is reduced to a series of subproblems. The preferred embodiment's implementation as described above, is then generalized to handle both the power constrained unconstrained (negligible noise) objective functions.
For the channel capacity value D21, the network performs the optimization
where U(m) is a collection of links in a given aggregate set m, k is a transmission mode index, reflecting the fact that a single link may transmit over multiple diversity channels, π1(k,q) is the transmit power for mode k and link q, γ(k,q) is the post beamforming signal to interference noise ration, and R1(m) is the total allowed transmit power for aggregate set m; by solving first the reverse link power control problem; then treating the forward link problem in an identical fashion, substituting the subscripts 2 for 1. The solution for the link-level optimization as described above is then implemented at each node, and the network solution derived therefrom.
The means used to solve D21 optimization are chosen to minimize the amount of auxiliary channel information, or network control information that displaces network content. For most of the max-min objective functions described herein, a necessary condition of optimality is that all of the links over each link index q achieve the same capacity. We can therefore require the constraint in Eq. 49 to be an equality For this embodiment, the objective function that is solved at each aggregate set m, becomes:
The preferred embodiment linearizes this objective function as a function of γ(q) and optimizes it using the formulation in EQ 28, EQ 29 and EQ 30.
For each aggregate set m, the network now attempts to achieve the given capacity objective, β, by (1) optimizing the receive beamformers, using simple MMSE processing, to simultaneously optimize the SINR; (2) based on the individual measured SINR for each q index, attempt to incrementally increase or lower its capacity as needed to match the current target; and (3) step the power by a quantized small step in the appropriate direction. When all aggregate sets have achieved the current target capacity, then the network can either increase the target capacity β, or add additional users (opportunistically or by signal) to exploit the now-known excess capacity. The network controller of the preferred embodiment is computationally extremely simple and requires a very small feedback channel, or portion of the control channel otherwise unused, to accomplish its tasks.
As the network evolves each independent channel is assigned a variable rate codec optimized for the currently achieved SINR for that link, whereby a code and associated rate are chosen to achieve the desired bit error using any of the Trellis Codes, interleavers, and/or Reed-Solomon codes known to the literature.
Good network performance includes, generally, a measure of uniform minimum performance level for all links assigned the same quality of service (QoS). The preferred embodiment uses Max-Min capacity criterion as disclosed above to attain this, as it is generalizable to a wide variety of network configurations. Minimizing the total power subject to arbitrary capacity constraints β(m), as in EQ 3 and EQ 4 is also an embodiment of interest and is easily accommodated by the current invention. The addition of reciprocity as a feature of the preferred embodiment allows us to state the decoupled objective function:
as the largest possible mutual information that can be obtained, as the one to be used to obtain network optimality.
The reciprocity equation, Eq. 2, establishes that the network's uplink capacity will equal its downlink capacity provided that the receive weights are used to transmit and the transmit weights to receive. Implementing the network optimization in this fashion provides the following benefits: (1) transmit weights can be obtained from receive weights; (2) transmit and receive weights require only local information at each node, thereby eliminating the ‘network God’ and ‘common knowledge’ problems; and (3) local optimization done using this optimizes the entire network, both making it stable and converging. The reciprocity equation is used particularly to tell each node how to choose its transmit weights optimally.
An improvement over blindly substituting transmit and receive weights, however, is in using the proper form of the objective function that satisfies the reciprocity equation, for that determines how to optimally adjust and select the gain over multiple outputs and multiple inputs. The choice of the objective function specified above dictates the algorithmic procedure that is also specified above to optimize the network.
Alternative embodiments of the network controller include having it set the entire network target capacity objective (β), using the network controller to add a node, drop a node, or change the target capacity objective for the nodes it governs or the network.
Although the present invention has been described chiefly in terms of the presently preferred embodiment, it is to be understood that the disclosure is not to be interpreted as limiting. Various alterations and modifications will no doubt become apparent to those skilled in the art after having read the above disclosure. Such modifications may involve other features which are already known in the design, manufacture and use of wireless electromagnetic communications networks, systems and MIMO networks and systems therefore, and which may be used instead of or in addition to features already described herein. The algorithms and equations herein are not limiting but instructive of the embodiment of the invention, and variations which are readily derived through programming or mathematical transformations which are standard or known to the appropriate art are not excluded by omission. Accordingly, it is intended that the appended claims are interpreted as covering all alterations and modifications as fall within the true spirit and scope of the invention in light of the prior art.
Additionally, although claims have been formulated in this application to particular combinations of elements, it should be understood that the scope of the disclosure of the present application also includes any single novel element or any novel combination of elements disclosed herein, either explicitly or implicitly, whether or not it relates to the same invention as presently claimed in any claim and whether or not it mitigates any or all of the same technical problems as does the present invention. The applicants hereby give notice that new claims may be formulated to such features and/or combinations of such features during the prosecution of the present application or of any further application derived therefrom.
Each link is given a unique link address (LA), defined by the transmit node address (TNA) and receive node address (RNA), i.e., the node address (NA) of the nodes originating and terminating that link, and the channel rank of that particular link if a true multirank MIMO link exists and is exploited by those nodes. In the sixteen-node network shown in
For certain levels of quality-of-service, the traffic packet may be followed by an acknowledgment packet sent from the receive node(s) back to the transmit node, comprising Nembed LPHY symbols. As in the TDD instantiation, the traffic and acknowledgement packets are separated by a guard time interval; however, the time interval between traffic and acknowledgement packets may be very short, e.g., on the order of the Short Interframe Space (SIFS) in 802.11 communications.
On the traffic path, data bits intended for each of Mport receive node are first encoded into Ndata complex traffic data symbols, e.g., complex QPSK or QAM symbols, using conventional encoder technology. These symbols are then multiplexed onto the upper Ndata input bins of an efficient orthogonal transform operator such as a fast Hadamard transform (FHT), such that at least Nembed bins of the transformation are not modulated by data during the subsequent transform operation. On the overhead or “pilot” path, at least one of Nembed bins is modulated. This bin location is chosen based on the node address of the transmit node, and a network-wide (e.g., time-of-day based) Transmission Security (TRANSEC) operation known to each node in the network, e.g., by modulo-Nembed adding the TNA to a common TRANSEC word, such that each node is modulating a unique bin during any given traffic or acknowledgement time slot. Each port of modulated traffic and pilot symbols are then passed through the fast orthogonal transformation, yielding NFHT transformed output symbols. Each port of transformed output symbols are then multiplied by a second, pseudorandom constant modulus TRANSEC receive code based on the node address of the receive node that the transmit node is attempting to communicate with over that port and time slot, and other information known only to the network users. The resultant symbols are then multiplied by the Mport×Mant transmit diversity weights employed at the transmitter, where Mant is the number of antennas employed at the transmitter. If those weights are determined adaptively, e.g., using knowledge of reciprocity of the communication channel, this data is further multiplied by a set of transmit-receive compensation weights that enforce reciprocity between the transmit and receive channels at the node. These symbols are then passed through the LPHY symbol modulator (in combination with symbols corresponding to other subcarriers if the transceiver employs an OFDM LPHY), and onto the subsequent DAC, upconversion, power amplification, and RF transmission operations.
After RF reception, downconversion, and ADC operations on Mant spatial or polarization diverse antennas, data received at each antenna is passed through an LPHY demodulator that converts that data to NFHT×.Mant complex matrix representation (Mant columns of NFHT×1 complex data), on each subcarrier if the transceiver is employing an OFDM LPHY. Each NFHT×1 complex data vector is then multiplied by the conjugate of the TRANSEC receive code for that node, which removes that TRANSEC receive code added at the transmitter for that port (and only for ports that were intended to pass data to that receive node). This data is then passed through the inverse of the orthogonal transformation employed at the transmitter, e.g., an inverse FHT, and separated into the lower Nembed output bins (an Nembed×Mant complex pilot data matrix) corresponding to the pilot signal(s) employed at each transmit node attempting to communicate with that receive node, and Ndata output bins (an Ndata×Mant complex traffic data matrix) corresponding to the traffic data transmitted to the receive node, as well as interference generated by other nodes in the network or external emitters.
On the pilot path, the Nembed×Mant complex pilot data matrix is then passed to an adaptation algorithm that detects bins modulated by the transmitters attempting to communicate with the receiver; identifies those transmitters based on the detected bins and the TRANSEC transmit code algorithm employed in the network, determines quality of the received pilot symbols and (by extension) traffic data, and develops combining weights that can extract the traffic data from the Ndata×Mant complex traffic data matrix at the maximum signal-to-interference-and-noise ratio (max-SINR) achievable by the transceiver. On the training path, these combiner weights are then applied to the traffic data matrix, and the extracted data is then passed to a traffic decoder that decodes the traffic data back into bits and performs additional operations employed by the communication link, e.g., bit-level data decryption and error detection operations.
In the preferred TDD and ad hoc embodiments, each transmitter uses an orthogonal transformation of the same length during its transmit operation, generating packets of the same time duration as well. However, in some alternate embodiments different transmitters may transmit signals with different numbers of traffic data symbols. In this case, if an appropriate orthogonal transformation is employed at the transmitters, e.g., a radix-2 FHT, and the pilot symbols are restricted to an appropriate subset of input bins in that transformation, e.g., the first Nembed=2p bins of an FHT, then the first Nembed symbols (or a multiple of the first Nembed symbols) can be used in the receive adaptation algorithm. This can allow the invention to be used in fully adhoc networks where nodes can transmit packets of arbitrary length. However, receive combiner weights obtained through this process may exhibit misadjustment relative to optimal weights for the traffic data, as the pilot symbols may not experience the full processing gain of the FHT.
Also described below is also describe an alternate embodiment in which the adaptive receive combining weights are adapted to completely separate intended links with maximum signal-to-interference ratio rather than SIR, i.e., to direct “hard nulls” at each transmitter attempting to communicate with the node during the receive time slot so that interlink interference is completely removed during the receive operation, and to direct corresponding hard nulls back at the intended links during subsequent transmission opportunities. Although this algorithm does introduce some misadjustment (particularly during receive operations), it can improve the stability of the LEGO transmit adaptation algorithms in highly dynamic communication networks.
The resultant fully-adaptive system provides a much more versatile solution than competing multilayer receive-adaptive techniques such as BLAST and STP, which only approaches the Shannon capacity in full-rank (i.e., high multipath) channels. In particular, the fully-adaptive system can use its diversity degrees of freedom (DoF's) to provide substantive transmit gain in low-rank channels found in airborne and rural conditions. The fully-adaptive system can also use these DoF's to excise interference impinging on either end of the link due to other nodes operating in the same frequency band, hostile jamming of the link. In addition, the fully adaptive system provides an automatic power control mechanism (LEGO Algorithm) that can be used to maximize capacity (high throughput applications) or minimize transmit power (LPD applications), depending on the requirements of the system at any point during a mission. These attributes greatly increase the flexibility, range of operation, and application of the approach.
Moreover, the weight adaptation procedure is decoupled from operations used to encode data onto each transmitter port (channel eigenmode) and decode data taken from the corresponding receive port at the other side of the link, without expensive multilayer decoding, encoding, and cancellation procedures employed in receive-adaptive methods. This greatly reduces the complexity and reaction time of the transceiver, by allowing the transmit/receive weights to quickly adjust to highly dynamic environments encountered in military use scenarios.
The fully adaptive point-to-point link seamlessly extends to collaborative multimode networks. In particular, each transceiver employs the same multiport linear processor structure to form simultaneous links to multiple neighbors in multinode networks. The resultant MIMO networking strategy (first disclosed in [1]) exploits the additional inherent route diversity of star, ring, or mesh networks, to provide the benefits of MIMO processing in a network setting.
The MIMO network illustrated in
The symbol and timing structure for the baseline Phase 1 Lower-PHY (LPHY) is illustrated in
The multiport PHY transmit/receive operations are illustrated in
After the FHT, the combined information and transmit pilot are modulated by a pseudorandom receive TRANSEC pilot, unique to the intended receiver or receive node address (RNA) in the network. If needed, the TRANSEC pilot is also frequency compensated to remove Doppler shift anticipated at the intended receiver, allowing the link to operate effectively velocities much higher than those anticipated in the Phase I demo. The TRANSEC output data is then passed through a linear distribution network (transmit beamformer) that can place beams in the direction of up to MANT targeted receive nodes for a transceiver with MANT transmit antennas. Alternately, the transmit beamformer can place up to 20 log10(MANT) energy in the direction of a single targeted receive node, allowing effective data transfer at a much lower power and consequent intercept footprint.
At the receiver, the processor strips off the receive TRANSEC pilot, simultaneously scrambling signals from any unauthorized user, and revealing the unique transmit pilots from each authorized user attempting to contact the receiver during that receive frame. The resultant data is passed to a computationally efficient, Modified Graham-Schmidt Orthogonalization (MGOS) based joint detection and signal extraction processor, which simultaneously detects each authorized pilot in the environment, and develops multiport receive combiner weights that excise interference in the communication channel (including self-interference from other authorized users). These weights are dewhitened and applied to the information-bearing signal after the inverse FHT (IFHT), and used to adapt retrodirective distribution weights used during subsequent transmit operations. The combined receive TRANSEC and IFHT spreading operations guarantees that the receive algorithm will apply equal interference rejection to the pilot and data symbols, for any interferer impinging on a node during its receive time slot.
The baseline system employs network-wide collaborative link optimization rules referred to as locally enabled global optimization (LEGO), which optimize network-wide measures of network quality using retrodirective transmit weight adaptation and local (link layer) power management instructions. The approach exploits the ability to form nulls during transmit operations to intelligently manage interference presented to other nodes transmitting or communicating in the same frequency channel. This includes other nodes operating on the same network, and nodes operating on disconnected networks, without the need for higher-layer cross-communication between interference nodes or networks. This capability, originally developed for optimization of point-to-multipoint cellular networks in wireless local-loop (AT&T Wireless Project Angel) and wireless metropolitan area networks (IEEE 802.16), cannot be employed in systems that do not perform transmit adaptation. This method and system can extend the LEGO approach to game theoretic methods that may perform this network optimization over a wider range of applications, networks, and optimization criteria.
The complexity of the method and system are shown in
The overall complexity of the DSP component of transceiver operations (adaptive algorithm) is less than 200 kcps for the phase 1 system, or well within the capabilities of a low-cost DSP components. Similarly, the overall complexity of the FPGA component of transceiver operations is less than 30 Mcps, which easily fits on the commercially available FPGA and in fact can be implemented using moderate cost DSP components.
Assuming low power rate-1 BPSK QAM encoding on each link, the resultant system can provide a PHY throughput (data rate into the MAC layer) of 96 kbps (192 kbps full duplex), or establish 12 simultaneous 48 kbps full-duplex data links (576 kbps network transfer rate) between each node-pair in a four-node ring network, using a single two-channel transceiver at each node in that network and a simple 250 ksps PAM lower PHY. Assuming an active bandwidth of 250 kHz, this corresponds to a spectral network efficiency of over 2 bps/Hz. This efficiency and bandwidth scales linearly with the number bits/symbol employed in the QAM encoding operation. Because the ring network establishes a counterrotating ring that can transfer data over two simultaneous routes, the reliability of the collaborative network increases dramatically—by over 2 nines if each node in the network as a two nines reliability!
Parameter Definitions, Equations and Glossary
Network Parameters:
μLA (mRNA , mTNA) = mLA
μNA (μRLA (mLA)) = [mRNA mTNA]
Datalink Parameters:
no data transported)
none
Data and Parameter Arrays:
Upper-PHY Signal Processing Operations:
Transmit Operations
Starting with the transmit bits BTNA(mframe) to be transmitted over logical subcarrier msub and logical frame mframe, perform the following operations.
where “.*” denotes the element-wise multiply operation, and 1M is the M×1 all-ones vector.
Two algorithms are specified here to adapt transmit distribution weights {GTNA(msub,mframe)},
Starting with the multiantenna data XRNA(msub,mframe) received and OFDM-demodulated over logical subcarrier msub and logical frame mframe, perform the following operations.
Step RP1: Remove the receive pilot, and inverse-FHT descrambled data
Step RP2: Separate pilot & data components
PRNA(msub,mframe)=SpilotTYRNA(msub,mframe) (2.2.2)
XRNA(msub,mframe)=SdataTYRNA(msub,mframe) (2.2.3)
Steps RA, Detect transmit pilots and estimate their SINR's γLA(msub,mframe) (Sections 3.1, 4.1).
TA:
Two algorithms are specified here to adapt receive combiner weights {WRNA(msub,mframe)},
Starting with the multiantenna received and deembedded pilot data (referred to as PRNA(msub) or PRNA as appropriate to simplify arguments) received and OFDM-demodulated over logical subcarrier msub and logical frame mframe, perform the following operations
Step RA1: Compute QRD of PRNA(msub,mframe)
Step RA2: Detect candidate TNA transmit pilots and spatially whitened adaptation weights
Step RA3: If spatial signature estimates Aport(msub) are available, where Aport(msub) is the import column of ARNA(msub) (see Step RA6), associate detected transmit pilots with receive ports and link addresses.
Step RA4: Drop and add ports, based on the port matching statistic cmatch (mdet,mport).
Step RA5: Assign receive ports and link statistics ηLA(msub) and γLA(msub).
ηLA(mport;msub)=ηdet(mdet(mport);msub) (3.1.22)
γLA(mport;msub)=γdet(mdet(mport);msub) (3.1.23)
u(mport;msub)=udet(mdet(mport);msub) (3.1.24)
U(msub)=[u(1;msub) . . . u(Mport;msub)] (3.1.25)
Step RA6: Estimate spatial steering matrices {ARNA(msub,mframe)}.
ARNA(msub,mframe)=CH(msub)U(msub) (3.1.26)
Step RA7: Compute combiner weights {WRNA(msub,mframe)}.
WRNA(msub,mframe)=C(msub)U(msub) (3.1.27)
Adaptive Transmit Algorithm
Starting with the receive weights WRNA(msub,mframe) given in (3.1.27) and target SINR's γRLA for the return link, perform the following operations.
Step TA1: Scale whitened transmit weights,
Step TA2: Compute distribution weights {GTNA(msub,mframe)}.
The target SINR's can also be derived from rate targets based on performance of the codec's used in the system, or from capacity targets {CRLA(mRLA)} or spectral efficiency targets {cRLA(mRLA)}, via the formula
cRLA(mRLA)=1.63CRLA(mRLA)/Wactive (3.2.4)
γRLA(mRLA)−λgap(2C
Where 1.63=1/0.6144 is the inverse efficiency of the airlink, which includes overhead for transmit pilots (0.9375 efficiency), the OFDM cyclic prefix (0.80 efficiency) and TDD framing (0.8192 efficiency), and where λgap is the SNR coding gap of the QAM codec. Target SINR, rate, or capacity can be set at either end of the link, i.e., as a transmitter design goal or as a control parameter passed from the link or network. In the first two cases, this adaptation is referred to here as locally enabled network optimization (LEGO).
Note that steps (3.2.1)-(3.2.3) adjust output power to meet a link SINR (or link rate or capacity) criterion. That is, the system will attempt to adjust transmit power at each node to meet this criterion. Also note that steps (3.2.1)-(3.2.3) require no information from other links in the network, including links originating from the same node. While this can be a highly desirable attribute in many applications, it has some drawbacks in practice. In particular, if the target SINR's are set too high, the resultant network can fail to converge and drive its transmitters into saturation. This event can be detected by computing the conducted power into each antenna,
and monitoring PRLA(mant) to ensure compliance with conducted power requirements.
In addition, the convergence time of the LEGO algorithm can be slow, especially during initial acquisition of multiple links. This performance can be improved by employing a max-SIR algorithm that forms hard nulls during the initial link acquisition period. This algorithm is easily implemented as an extension of the max-SINR algorithm described above.
Max-SIR Adaptation Algorithm
Adaptive Receive Algorithm
Starting with the multiantenna received and deembedded pilot data XPx(msub,mframe) (referred to as XPx(msub) or XPx as appropriate to simplify arguments) received and OFDM-demodulated over logical subcarrier msub and logical frame mframe, perform the following operations.
Adaptive Transmit Algorithm
Starting with the transmit weights WRNA(msub,mframe) given in (4.1.4) and target SINR's γRLA for the return link, perform the following operations.
Step TA1: Compute ηRLA(mport) using (3.2.1).
Step Compute power scaling π(mport;msub) using
TA1.1:
π(mport;msub)=ηRLA(mport)ηLA(mport;msub), (ηLA(mport;msub) given in (3.1.22)) (4.2.1)
Step TA2: Compute combiner weights {GRNA(msub,mframe)} using 3.2.3
The target SINR's can also be derived from rate targets based on performance of the codec's used in the system, or from capacity targets {CRLA(mRLA)} or spectral efficiency targets {cRLA(mRLA)}, using (3.2.4)-(3.2.5), and conducted power can be monitored using (3.2.6). In addition, the SINR and SIR estimates given by (3.1.23 and (4.1.3) can be used to detect “overloaded network” conditions where the max-SIR solution is misadjusting significantly from the max-SINR result.
While this invention has been described with reference to one or more illustrative embodiments, this description is not to be construed in a limiting sense. Various modifications and combinations of the illustrative embodiments, differing order of the sub-steps (including parallel and partial processing for one or more operations thereof), as well as other embodiments of the invention will be apparent to those skilled in the art upon referencing this disclosure. It is therefore intended this disclosure encompass any combination of the specifics described here and such modifications or embodiments. Furthermore, the scope of this invention includes any combination of the subordinate parts from the different embodiments disclosed in this specification, and is not limited to the specifics of the preferred embodiment or any of the alternative embodiments mentioned above. Individual configurations and embodiments of this invention may contain all, or less than all, of those disclosed in the specification according to the needs and desires of that user. The claims stated herein should further be read as including those elements which are not necessary to the invention yet are in the prior art, particularly that referenced and incorporated herein thereby, and are necessary to the overall function of that particular claim, and should be read as including, to the maximum extent permissible by law, known functional equivalents to the specification's disclosure, even though those functional equivalents are not exhaustively detailed herein or individually claimed below due to the legal preferences for limiting the number of claims and the law's intent to negate any requirement for combinatorial explosion for overly-detailed description and claiming of known and foreseeable alternatives.
Although the present invention has been described chiefly in terms of the presently preferred embodiment, it is to be understood that the disclosure is not to be interpreted as limiting. Various alterations and modifications will no doubt become apparent to those skilled in the art after having read the above disclosure. Such modifications may involve other features which are already known in the design, manufacture and use of adaptive and transitional MIMO systems, both hardware and associated software therefore, and which may be used instead of or in addition to features already described herein. The examples herein are not limiting but instructive of the embodiment of the invention, and variations which are readily derived through programming or embedded hardware transformations which are standard or known to the appropriate art are not excluded by omission. Accordingly, it is intended that the appended claims are interpreted as covering all alterations and modifications as fall within the true spirit and scope of the invention in light of the prior art.
Additionally, although claims have been formulated in this application to particular combinations of elements, it should be understood that the scope of the disclosure of the present application also includes any single novel element or any novel combination of elements disclosed herein, either explicitly or implicitly, whether or not it relates to the same invention as presently claimed in any claim and whether or not it mitigates any or all of the same technical problems as does the present invention. The applicants hereby give notice that new claims may be formulated to such features and/or combinations of such features during the prosecution of the present application or of any further application derived there from.
The present application is a continuation of and claims priority to U.S. patent application Ser. No. 11/880,825, filed Jul. 23, 2007, which is a continuation in part of and claims priority to patent application Ser. No. 09/878,789, filed on Jun. 10, 2001, issued as U.S. Pat. No. 7,248,841 on Jul. 24, 2007, which are incorporated herein by reference in their entirety for all purposes.
| Number | Name | Date | Kind |
|---|---|---|---|
| 5093781 | Castelaz | Mar 1992 | A |
| 5255210 | Gardner et al. | Oct 1993 | A |
| 5260968 | Gardner et al. | Nov 1993 | A |
| 5299148 | Gardner et al. | Mar 1994 | A |
| 5459725 | Bodner et al. | Oct 1995 | A |
| 5471647 | Gerlach et al. | Nov 1995 | A |
| 5515378 | Roy, III et al. | May 1996 | A |
| 5539832 | Weinstein et al. | Jul 1996 | A |
| 5546090 | Roy, III et al. | Aug 1996 | A |
| 5550810 | Monogioudis et al. | Aug 1996 | A |
| 5574979 | West | Nov 1996 | A |
| 5592490 | Barratt et al. | Jan 1997 | A |
| 5625880 | Goldburg et al. | Apr 1997 | A |
| 5634199 | Gerlach et al. | May 1997 | A |
| 5642353 | Roy, III et al. | Jun 1997 | A |
| 5724666 | Dent | Mar 1998 | A |
| 5771352 | Nakamura et al. | Jun 1998 | A |
| 5790516 | Gudmundson et al. | Aug 1998 | A |
| 5809019 | Ichihara et al. | Sep 1998 | A |
| 5828658 | Ottersten et al. | Oct 1998 | A |
| 5852633 | Levin et al. | Dec 1998 | A |
| 5870385 | Ahmadi et al. | Feb 1999 | A |
| 5875186 | Belanger et al. | Feb 1999 | A |
| 5886988 | Yun et al. | Mar 1999 | A |
| 5890055 | Chu et al. | Mar 1999 | A |
| 5909470 | Barratt et al. | Jun 1999 | A |
| 5909471 | Yun | Jun 1999 | A |
| 5912921 | Warren et al. | Jun 1999 | A |
| 5930243 | Parish et al. | Jul 1999 | A |
| 6006110 | Raleigh | Dec 1999 | A |
| 6016313 | Foster, Jr. et al. | Jan 2000 | A |
| 6023203 | Parish | Feb 2000 | A |
| 6037898 | Parish et al. | Mar 2000 | A |
| 6044062 | Brownrigg et al. | Mar 2000 | A |
| 6047023 | Arnstein | Apr 2000 | A |
| 6047189 | Yun et al. | Apr 2000 | A |
| 6061389 | Ishifuji et al. | May 2000 | A |
| 6067290 | Paulraj et al. | May 2000 | A |
| 6069894 | Holender et al. | May 2000 | A |
| 6088327 | Muschallik et al. | Jul 2000 | A |
| 6097703 | Larsen et al. | Aug 2000 | A |
| 6097707 | Hodzic et al. | Aug 2000 | A |
| 6101399 | Raleigh | Aug 2000 | A |
| 6104712 | Robert et al. | Aug 2000 | A |
| 6108565 | Scherzer | Aug 2000 | A |
| 6115406 | Mesecher | Sep 2000 | A |
| 6115580 | Chuprun et al. | Sep 2000 | A |
| 6128276 | Agee | Oct 2000 | A |
| 6141567 | Youssefmir et al. | Oct 2000 | A |
| 6144711 | Raleigh et al. | Nov 2000 | A |
| 6154661 | Goldburg, II | Nov 2000 | A |
| 6177906 | Petrus | Jan 2001 | B1 |
| 6185440 | Barratt et al. | Feb 2001 | B1 |
| 6219341 | Varanasi | Apr 2001 | B1 |
| 6243369 | Grimwood et al. | Jun 2001 | B1 |
| 6266528 | Farzaneh | Jul 2001 | B1 |
| 6275543 | Petrus et al. | Aug 2001 | B1 |
| 6300881 | Yee et al. | Oct 2001 | B1 |
| 6317411 | Whinnett et al. | Nov 2001 | B1 |
| 6353604 | Grimwood et al. | Mar 2002 | B2 |
| 6359923 | Agee et al. | Mar 2002 | B1 |
| 6362781 | Thomas et al. | Mar 2002 | B1 |
| 6373432 | Rabinowitz et al. | Apr 2002 | B1 |
| 6377631 | Raleigh | Apr 2002 | B1 |
| 6377636 | Paulraj et al. | Apr 2002 | B1 |
| 6411799 | Padovani | Jun 2002 | B1 |
| 6441784 | Flore et al. | Aug 2002 | B1 |
| 6459171 | Leifer | Oct 2002 | B1 |
| 6463295 | Yun | Oct 2002 | B1 |
| 6473467 | Wallace et al. | Oct 2002 | B1 |
| 6493331 | Walton et al. | Dec 2002 | B1 |
| 6493335 | Darcie et al. | Dec 2002 | B1 |
| 6504506 | Thomas et al. | Jan 2003 | B1 |
| 6512737 | Agee | Jan 2003 | B1 |
| 6564036 | Kasapi | May 2003 | B1 |
| 6570527 | Lindskog et al. | May 2003 | B1 |
| 6594473 | Dabak et al. | Jul 2003 | B1 |
| 6600914 | Uhlik et al. | Jul 2003 | B2 |
| 6615024 | Boros et al. | Sep 2003 | B1 |
| 6621851 | Agee et al. | Sep 2003 | B1 |
| 6633616 | Crawford | Oct 2003 | B2 |
| 6647078 | Thomas et al. | Nov 2003 | B1 |
| 6650714 | Dogan et al. | Nov 2003 | B2 |
| 6650881 | Dogan | Nov 2003 | B1 |
| 6651210 | Trott et al. | Nov 2003 | B1 |
| 6654590 | Boros et al. | Nov 2003 | B2 |
| 6668161 | Boros et al. | Dec 2003 | B2 |
| 6683915 | Trott et al. | Jan 2004 | B1 |
| 6684366 | Trott et al. | Jan 2004 | B1 |
| 6687492 | Sugar et al. | Feb 2004 | B1 |
| 6690747 | Petrus et al. | Feb 2004 | B2 |
| 6711412 | Tellado et al. | Mar 2004 | B1 |
| 6731689 | Dogan | May 2004 | B2 |
| 6731705 | Kasapi et al. | May 2004 | B2 |
| 6735258 | Trott et al. | May 2004 | B1 |
| 6738020 | Lindskog et al. | May 2004 | B1 |
| 6747594 | Lindskog et al. | Jun 2004 | B2 |
| 6751187 | Walton et al. | Jun 2004 | B2 |
| 6757265 | Sebastian et al. | Jun 2004 | B1 |
| 6760599 | Uhlik | Jul 2004 | B1 |
| 6768747 | Dogan | Jul 2004 | B1 |
| 6771706 | Ling et al. | Aug 2004 | B2 |
| 6778513 | Kasapi et al. | Aug 2004 | B2 |
| 6785341 | Walton et al. | Aug 2004 | B2 |
| 6788948 | Lindskog et al. | Sep 2004 | B2 |
| 6795409 | Youssefmir et al. | Sep 2004 | B1 |
| 6795413 | Uhlik | Sep 2004 | B1 |
| 6801589 | Dogan | Oct 2004 | B1 |
| 6802038 | Yu | Oct 2004 | B1 |
| 6834249 | Orchard | Dec 2004 | B2 |
| 6836673 | Trott | Dec 2004 | B1 |
| 6839573 | Youssefmir et al. | Jan 2005 | B1 |
| 6839574 | Petrus et al. | Jan 2005 | B2 |
| 6865377 | Lindskog et al. | Mar 2005 | B1 |
| 6888882 | Dogan | May 2005 | B1 |
| 6888899 | Raleigh et al. | May 2005 | B2 |
| 6894993 | Yu et al. | May 2005 | B2 |
| 6915438 | Boros | Jul 2005 | B2 |
| 6928287 | Trott et al. | Aug 2005 | B2 |
| 6931030 | Dogan | Aug 2005 | B1 |
| 6931262 | Steele | Aug 2005 | B2 |
| 6931583 | Trott et al. | Aug 2005 | B2 |
| 6937665 | Vandenameele | Aug 2005 | B1 |
| 6950630 | Chauvin et al. | Sep 2005 | B2 |
| 6950979 | Zhang et al. | Sep 2005 | B2 |
| 6954643 | Petrus | Oct 2005 | B2 |
| 6957042 | Williams | Oct 2005 | B2 |
| 6961388 | Ling et al. | Nov 2005 | B2 |
| 6963742 | Boros et al. | Nov 2005 | B2 |
| 6965774 | Kasapi et al. | Nov 2005 | B1 |
| 6965788 | Barratt et al. | Nov 2005 | B1 |
| 6970682 | Crilly, Jr. et al. | Nov 2005 | B2 |
| 6973314 | Wilson et al. | Dec 2005 | B2 |
| 6982968 | Barratt et al. | Jan 2006 | B1 |
| 6983127 | Da Torre et al. | Jan 2006 | B1 |
| 6985466 | Yun et al. | Jan 2006 | B1 |
| 6987819 | Thomas et al. | Jan 2006 | B2 |
| 6996060 | Dahlby et al. | Feb 2006 | B1 |
| 6996163 | Bhora et al. | Feb 2006 | B2 |
| 6999771 | Kasapi et al. | Feb 2006 | B1 |
| 6999794 | Lindskog et al. | Feb 2006 | B1 |
| 7003310 | Youssefmir et al. | Feb 2006 | B1 |
| 7006579 | Kuchi et al. | Feb 2006 | B2 |
| 7016429 | Dogan et al. | Mar 2006 | B1 |
| 7020072 | Li et al. | Mar 2006 | B1 |
| 7020107 | Kasapi et al. | Mar 2006 | B2 |
| 7020490 | Khatri | Mar 2006 | B2 |
| 7024163 | Barratt et al. | Apr 2006 | B1 |
| 7027415 | Dahlby et al. | Apr 2006 | B1 |
| 7027523 | Jalali et al. | Apr 2006 | B2 |
| 7027837 | Uhlik et al. | Apr 2006 | B1 |
| 7031679 | Persson et al. | Apr 2006 | B2 |
| 7035358 | Sankaran et al. | Apr 2006 | B1 |
| 7035661 | Yun | Apr 2006 | B1 |
| 7036067 | Roy et al. | Apr 2006 | B2 |
| 7039016 | Lindskog et al. | May 2006 | B1 |
| 7039363 | Kasapi et al. | May 2006 | B1 |
| 7042856 | Walton et al. | May 2006 | B2 |
| 7043259 | Trott | May 2006 | B1 |
| 7047045 | Steele et al. | May 2006 | B2 |
| 7050832 | Kasapi | May 2006 | B2 |
| 7054301 | Sousa et al. | May 2006 | B1 |
| 7062294 | Rogard et al. | Jun 2006 | B1 |
| 7068628 | Li et al. | Jun 2006 | B2 |
| 7072413 | Walton et al. | Jul 2006 | B2 |
| 7079480 | Agee | Jul 2006 | B2 |
| 7106781 | Agee et al. | Sep 2006 | B2 |
| 7110349 | Branlund et al. | Sep 2006 | B2 |
| 7110381 | O'Sullivan | Sep 2006 | B1 |
| 7111223 | Roy et al. | Sep 2006 | B2 |
| 7116722 | Foschini et al. | Oct 2006 | B2 |
| 7133459 | Onggosanusi et al. | Nov 2006 | B2 |
| 7139592 | Leifer et al. | Nov 2006 | B2 |
| 7149238 | Agee et al. | Dec 2006 | B2 |
| 7149239 | Hudson | Dec 2006 | B2 |
| 7151795 | Goldburg | Dec 2006 | B1 |
| 7158493 | Uhlik et al. | Jan 2007 | B1 |
| 7164669 | Li et al. | Jan 2007 | B2 |
| 7164726 | Trott | Jan 2007 | B1 |
| 7164739 | Trott | Jan 2007 | B1 |
| 7206293 | Kasapi et al. | Apr 2007 | B2 |
| 7206554 | Lindskog | Apr 2007 | B1 |
| 7221699 | Lindskog | May 2007 | B1 |
| 7227855 | Barratt et al. | Jun 2007 | B1 |
| 7242720 | Sugiyama et al. | Jul 2007 | B2 |
| 7248841 | Agee et al. | Jul 2007 | B2 |
| 7257101 | Petrus et al. | Aug 2007 | B2 |
| 7263082 | Lindskog | Aug 2007 | B1 |
| 7266685 | Meandzija et al. | Sep 2007 | B1 |
| 7269389 | Petrus et al. | Sep 2007 | B2 |
| 7277679 | Barratt et al. | Oct 2007 | B1 |
| 7299071 | Barratt et al. | Nov 2007 | B1 |
| 7299073 | Wilson et al. | Nov 2007 | B2 |
| 7302565 | Meandzija et al. | Nov 2007 | B2 |
| 7310538 | Steele et al. | Dec 2007 | B2 |
| 7336626 | Barratt et al. | Feb 2008 | B1 |
| 7336719 | Gore et al. | Feb 2008 | B2 |
| 7339906 | Dahlby et al. | Mar 2008 | B1 |
| 7339908 | Uhlik et al. | Mar 2008 | B2 |
| 7339981 | Dogan | Mar 2008 | B2 |
| 7342912 | Kerr et al. | Mar 2008 | B1 |
| 7349371 | Schein et al. | Mar 2008 | B2 |
| 7352774 | Uhlik et al. | Apr 2008 | B2 |
| 7362799 | Petrus | Apr 2008 | B1 |
| 7363376 | Uhlik et al. | Apr 2008 | B2 |
| 7366223 | Chen et al. | Apr 2008 | B1 |
| 7372911 | Lindskog et al. | May 2008 | B1 |
| 7386043 | Goldburg | Jun 2008 | B2 |
| 7386781 | Persson et al. | Jun 2008 | B2 |
| 7389111 | Petrus | Jun 2008 | B2 |
| 7397804 | Dulin et al. | Jul 2008 | B2 |
| 7406261 | Shattil et al. | Jul 2008 | B2 |
| 7406315 | Uhlik et al. | Jul 2008 | B2 |
| 7411977 | Chen et al. | Aug 2008 | B1 |
| 7418043 | Shattil et al. | Aug 2008 | B2 |
| 7420984 | Chen et al. | Sep 2008 | B2 |
| 7424002 | Barratt et al. | Sep 2008 | B2 |
| 7430197 | Uhlik et al. | Sep 2008 | B1 |
| 7430246 | Trott | Sep 2008 | B1 |
| 7430606 | Meandzija et al. | Sep 2008 | B1 |
| 7433347 | Trott et al. | Oct 2008 | B1 |
| 7433418 | Dogan et al. | Oct 2008 | B1 |
| 7460835 | Petrus | Dec 2008 | B1 |
| 7594010 | Dohler et al. | Sep 2009 | B2 |
| 7606204 | Sebastian et al. | Oct 2009 | B2 |
| 7688710 | Wu et al. | Mar 2010 | B2 |
| 7760814 | Jungnickel et al. | Jul 2010 | B2 |
| 7801247 | Onggosani et al. | Sep 2010 | B2 |
| 7813441 | Jalali et al. | Oct 2010 | B2 |
| 7986742 | Ketchum et al. | Jul 2011 | B2 |
| 8098568 | Laroia et al. | Jan 2012 | B2 |
| 20010033622 | Jongreen et al. | Oct 2001 | A1 |
| 20020062472 | Medlock et al. | May 2002 | A1 |
| 20020111142 | Klimovitch | Aug 2002 | A1 |
| 20020111144 | Schiff | Aug 2002 | A1 |
| 20020122381 | Wu | Sep 2002 | A1 |
| 20020122465 | Agee et al. | Sep 2002 | A1 |
| 20020150109 | Agee | Oct 2002 | A1 |
| 20020155818 | Boros et al. | Oct 2002 | A1 |
| 20020159506 | Alamouti et al. | Oct 2002 | A1 |
| 20020193146 | Wallace et al. | Dec 2002 | A1 |
| 20030072382 | Raleigh et al. | Apr 2003 | A1 |
| 20030086366 | Branlund et al. | May 2003 | A1 |
| 20030087673 | Walton et al. | May 2003 | A1 |
| 20030123384 | Agee | Jul 2003 | A1 |
| 20030124976 | Tamaki et al. | Jul 2003 | A1 |
| 20040012387 | Shattil | Jan 2004 | A1 |
| 20040095907 | Agee et al. | May 2004 | A1 |
| 20040100897 | Shattil | May 2004 | A1 |
| 20040131025 | Dohler et al. | Jul 2004 | A1 |
| 20040180625 | Steele et al. | Sep 2004 | A1 |
| 20040193982 | Bhora et al. | Sep 2004 | A1 |
| 20050147076 | Sadowsky et al. | Jul 2005 | A1 |
| 20050197748 | Holst et al. | Sep 2005 | A1 |
| 20060193373 | Agee et al. | Aug 2006 | A1 |
| 20070009012 | Carrivan et al. | Jan 2007 | A1 |
| 20070042753 | Uhlik et al. | Feb 2007 | A1 |
| 20070133496 | Barratt et al. | Jun 2007 | A1 |
| 20070140374 | Raleigh et al. | Jun 2007 | A1 |
| 20070165552 | Kasapi et al. | Jul 2007 | A1 |
| 20070268980 | Brannstorm et al. | Nov 2007 | A1 |
| 20070283230 | Brannstrom et al. | Dec 2007 | A1 |
| 20080075033 | Shattil | Mar 2008 | A1 |
| 20080088517 | Ansari et al. | Apr 2008 | A1 |
| 20080090575 | Barak et al. | Apr 2008 | A1 |
| 20080101502 | Navidpour et al. | May 2008 | A1 |
| 20080130611 | Branlund et al. | Jun 2008 | A1 |
| 20080181170 | Branlund et al. | Jul 2008 | A1 |
| 20080232238 | Agee | Sep 2008 | A1 |
| 20090116541 | Farrokhi et al. | May 2009 | A1 |
| 20100020907 | Rezvani et al. | Jan 2010 | A1 |
| 20100080317 | Narasimhan et al. | Apr 2010 | A1 |
| 20100111141 | Currivan et al. | May 2010 | A1 |
| 20100162075 | Brannstrom et al. | Jun 2010 | A1 |
| 20100174599 | Rosenblatt et al. | Jul 2010 | A1 |
| 20100214145 | Narasimhan et al. | Aug 2010 | A1 |
| 20100246454 | Ansari et al. | Sep 2010 | A1 |
| 20100254325 | Narasimhan et al. | Oct 2010 | A1 |
| 20100303160 | Rezvani et al. | Dec 2010 | A1 |
| 20110019608 | Dohler et al. | Jan 2011 | A1 |
| Number | Date | Country |
|---|---|---|
| 1744575 | Jan 2007 | EP |
| 2239990 | Oct 2010 | EP |
| 2264965 | Dec 2010 | EP |
| 2317664 | May 2011 | EP |
| 9120135 | Dec 1991 | WO |
| 9809395 | Mar 1998 | WO |
| WO9827669 | Jun 1998 | WO |
| WO9837638 | Aug 1998 | WO |
| WO9837638 | Aug 1998 | WO |
| WO9908451 | Feb 1999 | WO |
| WO9908451 | Feb 1999 | WO |
| WO9940689 | Aug 1999 | WO |
| WO0011823 | Mar 2000 | WO |
| WO0025485 | May 2000 | WO |
| Entry |
|---|
| L. Delaney, B. Agee, M. Buckner, R. Buehrer, J. Cafarella, P. Dickenson, J. Freebersyser, R. Gonzales, A. Ivers, . Kind, G. Minden, J. Mitola, R. Nowak, G. Roussos, R. Tingley, C. Ford, N. Haller, Toward a Universal Radio Frequency System for Special Operations Forces, National Research Council, 2009, Washington, DC: The National Academies Press. |
| Bromberg, M., Agee, B., “Optimization of Spatially Adaptive Reciprocal Multipoint Communication Networks,” IEEE Transactions on Communications, vol. 51, No. 8, Aug. 2003. |
| B. Agee, R. Kleinman, J. Reed, “Soft Synchronization of Direct Sequence Spread Spectrum Signals,” IEEE Trans. Comm., vol. COM-44, No. 11, pp. 1527-1536, Nov. 1996. |
| B. Agee, “Solving the Near-Far Problem: Exploitation of Spatial and Spectral Diversity in Wireless Personal Communication Networks,” (1994). |
| R. Mendoza, J. Reed, T. Hsia, B. Agee, “Interference Rejection Using Time-Dependent Constant Modulus Algorithms and a Hybrid CMA/SCD,” IEEE Trans. ASSP, Jul. 1991. |
| B. Agee, S. Schell, W. Gardner, “Spectral Self-Coherence Restoral: A New Approach to Blind Adaptive Signal Extraction Using Antenna Arrays,” IEEE Proceedings, vol. 78, No. 4, pp. 753-767, Apr. 1990. |
| B. Agee, M. Bromberg, “Exploitation of MIMO Network Diversity in Multipoint Communication Networks,” presented at the Thirty-Fifth Asilomar Conference Signals, Systems, and Computers, Nov. 2001. |
| M. Bromberg, B. Agee, “The LEGO Approach for Achieving Max-Min Capacity in Reciprocal Multipoint Networks,” in Proc. Thirty Fourth Asilomar Conf. Signals, Systems, and Computers, Oct. 2000. |
| B. Agee, “Exploitation of Internode MIMO Channel Diversity in Spatially Distributed Multipoint Networks,” in Proc. Tenth Annual Virginia Tech Symposium on Wireless Personal Comm., Jun. 2000. |
| B. Agee, S. Bruzzone, M. Bromberg, “Exploitation of Signal Structure in Array-Based Blind Copy and Copy-Aided DF Systems,” in Proc. 1998 Intl. Conf. on Acoustics, Speech and Signal Proc., pp. 2489-2492, vol. 4, May 1998. |
| M. Bromberg, B. Agee, “Direction Finding for Unstructured Emitters in the Presence of Structured Interferers,” in Proc. 1998 Intl. Conf. on Acoustics, Speech and Signal Processing, pp. 2537-2540, vol. 4, May 13, 1998. |
| E. Krzysiak, D. Branlund, B. Agee, “New Copy-Aided Techniques for Superresolution Direction Finding and Geolocation,” in Proc. Fourth Southwest Institute Conference on Direction Finding, Nov. 1997. |
| J. Treichler, B. Agee, “A New Approach to Multipath Correction of Constant Modulus Signals,” IEEE Trans. Acoustics, Speech and Signal Processing, vol. ASSP-31, No. 2, Apr. 1983. |
| W. Gardner, B. Agee, “Two-Stage Adaptive Noise Cancellation for Intermittent-Signal Applications,” IEEE Trans. Information Theory, vol. IT-26, No. 6, Nov. 1980. |
| B. Agee, “Blind Detection, Separation, and Location of Dense Co-Channel Emitters Using Multiplatform Spatial-Coherence Restoral,” in Proc. 2006 IEEE Workshop on Sensor Array and Multichannel Processing, Jul. 2006. |
| J. Fite, S. Bruzzone, B. Agee, “Blind Separation of Voice Modulated Single-Side-Band Using the Multi-Target Variable Modulus Algorithm,” in Proc. 1996 Conf. on Acoustics, Speech and Signal Processing, pp. 2726-2729, vol. 5, May 1996. |
| B. Agee, S. Bruzzone, “Exploitation of Signal Structure in Array-Based Blind Copy and Copy-Aided DF Systems,” in Proc. 1994 CRASP Conf. on Co-Channel Demodulation, Jun. 1994. |
| B. Agee, “On the Performance Bounds Adhered to by Copy-Aided DF Algorithms,” presented at Twenty-Seventh Asilomar Conference on Signals, Systems and Computers, Nov. 1993. |
| B. Agee, K. Cohen, J. Reed, T. Hsia, “Simulation Performance of a Blind Adaptive Array for a Realistic Mobile Channel,” in Proc. 1993 Conf. on Vehicular Technology, May 18, 1993. |
| B. Agee, J. Reed, “A Technique for Instantaneous Tracking of Frequency Hopping Signals in the Presence of Spectrally Correlated Interference,” in Proc. Twenty-Sixth Asilomar Conference on Signals, Systems and Computers, Nov. 1992. |
| B. Agee, The Property Restoral Approach to Blind Adaptive Signal Extraction, Ph.D. Dissertation, Dept. of Electrical Engineering, University of California, Davis, CA, Jun. 1989. |
| B. Agee, “Maximum-Likelihood Approaches to Blind Adaptive Signal Extraction Using Narrowband Antenna Arrays,” in Proc. Twenty-Fifth Asilomar Conference on Signals, Systems and Computers, Nov. 1991. |
| B. Agee, “The Copy/DF Approach to Signal-Specific Emitter Location,” in Proc. Twenty-Fifth Asilomar Conference on Signals, Systems and Computers, Nov. 1991. |
| B. Agee, R. Calabretta, “ARMA-Like and ML-Like Copy/DF Approaches for Signal-Specific Emitter Location,” in Proc. Fifth ASSP Workshop on Spectrum Estimation and Modelling, 1990. |
| B. Agee, S. Venkataraman, “Adaptive Demodulation of PCM Signals in the Frequency Domain,” in Proc. Twenty-Third Asilomar Conference on Signals, Systems and Computers, Nov. 1989. |
| S. Schell, B. Agee, “Application of the SCORE Algorithm and SCORE Extensions to Sorting in the Rank-L Spectral Self-Coherence Environment,” in Proc. Twenty-Second Asilomar Conf. Signals, Systems, Comp., Nov. 1988. |
| B. Agee, “The Baseband Modulus Restoral Approach to Blind Adaptive Signal Demodulation,” 1988 Digital Signal Processing Workshop, Sep. 1988. |
| B. Agee, S. Schell, W. Gardner, “The SCORE Approach to Blind Adaptive Signal Extraction: An Application of the Theory of Spectral Correlation,” in Proc. Fourth ASSP Workshop on Spectrum Estimation and Modelling, p. 277, Aug. 1988. |
| B. Agee, “The Least-Squares CMA: A New Approach to Rapid Correction of Constant Modulus Signals,” in Proc. 1986 International Conf. on Acoustics, Speech and Signal Processing, vol. 2, p. 19.2.1, Apr. 1986, Tokyo, Japan. |
| M.C. Bromberg, B. Agee, “Transmit Beamforming for Optimizing Network Capacity”, Jun. 26, 2001. |
| B. Agee, “Blind Separation and Capture of Communication Signals Using a Multitarget Constant Modulus Beamformer,” Adapted from a paper published in Proc. 1989 IEEE Military Comm. Conference, Oct. 1989. |
| B. Agee, “Fast Acquisition of Burst and Transient Signals Using a Predictive Adaptive Beamformer,” Adapted from a paper published in Proc. 1989 IEEE Military Communications Conference, Oct. 1989. |
| B. Agee, “Convergent Behavior of Modulus-Restoring Adaptive Arrays in Gaussian Interference Environments,” in Proc. Twenty-Second Asilomar Conf. Signals, Systems and Computers, Nov. 1988. |
| Swales, et. al., IEEE Trans. Veh. Technol. vol. 39 No. 1 Feb. 1990. |
| Agee, B., Reed, J., Kleinman, R., “Application of Eigenstructure Techniques to Blind Despreading of Direct-Sequence Spread Spectrum Signals,” Dec. 3, 1990. |
| Agee, B., “Array-Based Techniques for Wireless Telephony,” 1995 IEEE Communications Theory Workshop, Apr. 25, 1995. |
| Kelly, P., Agee, B., Radix Technologies, Inc.,“The Backtalk Communications Airlink,” Jan. 18, 1998. |
| B. Agee, C. Clark, Radix Technologies, “Technical Challenges to Network Based Commercial Geolocation, A Defense Technologist's Perspective,” 1997 MPRG Symposium on Wireless Personal Communications, Jun. 12, 1997. |
| Agee, B., “Efficient Allocation of RF Transceiver Resources in Spatially Adaptable Communication Networks,” Jun. 4, 2003. |
| Bromberg, M., “A Quick Primer on the LEGO Algorithm,” 2002. |
| Radix Technologies, “Live Data MT-CMA Test,” 1997. |
| Agee, B., “LMS/Part 15 Internetwork Interference Study Results-to-Date,” Feb. 23, 2001. |
| Agee, B., Kelly, Phillip, Radix Technologies, “The Backtalk™ Airlink for Full Exploitation of Spectral and Spatial Diversity in Wireless Communication Systems,” Fourth Workshop on Smart Antennas in Wireless Mobile Communications, Jul. 24, 1997. |
| Bromberg, M.C., “A Hilbert Space Algebra”, Masters Thesis in Mathematics, May 1986. |
| Bromberg, M.C., Brown, D., “The Use of Programmable DSPs in Antenna Array Processing,” May 28, 2008. |
| Bromberg, M.C., “Reliable, High Capacity, Multipoint, Wireless Information Networks,” Date Unknown. |
| Bromberg, M.C., “Optimizing MIMO Multipoint Wireless Networks Assuming Gaussian Other-User Interference.” IEEE Transactions on Information Theory, vol. 49, No. 10, Oct. 2003. (IEEE Proof). |
| Bromberg, M., Agee, B., “Using Channel Reciprocity to Optimize Network Capacity,” Oct. 2, 2000. |
| Agee, B. “Full-Duplex LPI/AJ Communications Using Blindly-Adapted Retrodirective Antenna Arrays,” Project “Backtalk” Final Report (Annotated Briefing), Mar. 31, 1994. |
| Agee, B., “Technical Evaluation of Wireless Broadband Issues & Equipment Alternatives EPRI,” BWA Technical Evaluation Final Report Nov. 19, 1999. |
| Agee, B., “4G Network Enhancements Using the ETSI Digital Audio Broadcast Standard,” DAB Application to 4G GWCom Network Jan. 4, 2001. |
| Agee, B., “Capacity Analysis of LMS Network Scenarios Operating in a Part 15 Network Environment,” Apr. 25, 1999. |
| Agee, B., “Introduction of Smart Antennas Into the Metricom Wireless Data Network Overview, Players, and Application,” Aug. 6, 1999. |
| Agee, B., “4G Airlink Enhancements to the Metricom Ricochet System,” 4G Airlink/Technology Concepts, Dec. 21, 1999. |
| Stutzman, W., Barts, M., “The Stub Loaded Helix Antenna,” 1999. |
| Agee, B., “Performance of the Multitarget Modulus Restoral Algorithm Against the Keystroke Data Set,” Apr. 6, 1990. |
| Agee, B., Young, D., “Blind Capture and Geolocation of General Spatially Self-Coherent Waveforms Using Multiplatform SCORE,” Maple Press, 1990. |
| Biedka, T., Agee, B., “Subinterval Cyclic MUSIC—Robust DF with Error in Cycle Frequency Knowledge,” 1991. |
| Bromberg, M., Agee, B., “The LEGO Approach for Achieving Max-Min Capacity in Reciprocal Multipoint Networks,” 2001. |
| Mendoza, R., Reed, J., Hsia, T., Agee, B., “Interference Rejection Using Time-Dependent Constant Modulus Algorithms,” 1989. |
| Schell, S., Calabretta, R., Gardner, W., Agee, B., “Cyclic MUSIC Algorithms for Signal-Selective Direction Estimation,” 1989. |
| Agee, B., Protean Radio Networks, “WLAN Reconnaissance,” Jan. 13, 2003. |
| Agee, B., Protean Radio Networks, “The PHY-IASM MIMO Network Capable Transceiver: Concept and IP Overview,” May 20, 2004. |
| Agee, B., “ORION Phase A Algorithms,” Final Design Review, Algorithms Overview Jul. 17-18, 1996 (FDR Date), Jul. 19, 1996. |
| Bromberg, M., Agee, B., “Using Channel Reciprocity to Optimize Network Capacity,” May 11, 2000. |
| Bromberg, M., Agee, B., “An Analysis of the LEGO Algorithm for Optimizing the Performance of Wireless Networks,” Thirty Fourth Asilomar Conf. Signals, Systems, and Computers, Oct. 2000, and in Proceedings of the Thirty Fifth Asilomar Conf. Signals, Systems, and Computers, Nov. 2001. |
| Bromberg, M. Agee, B., “The LEGO Technique for Optimizing the Performance of Wireless, MultiUser, MIMO Channel, TDD Networks,” Dec. 28, 2000. |
| Bromberg, M. Agee, B., “The LEGO Technique for Locally Enabled Global Optimization of Reciprocal Multinode Communication Networks,” Feb. 5, 2001. |
| Agee, B., “OFDN-Based SSICS Capabilities Demonstration,” Aug. 11, 2000. |
| Agee, B., Young, D., “Blind Capture and Geolocation of General Spatially Coherent Waveforms Using Multiplatform SCORE,” Presented in Proc. Twenty-Fourth Asilomar Conf. on Signals, Systems, and Computers, 1990. Maple Press. |
| Agee, B., Young, D., “Blind Capture and TDOA Estimation of Stationary Waveforms Using Multiplatform Temporal Cross Coherence Restoral,” Nov. 4, 1990. |
| Office Action Summary from U.S. Appl. No. 09/878,789 which was mailed on Apr. 25, 2005. |
| Office Action Summary from U.S. Appl. No. 09/878,789 which was mailed on Oct. 12, 2005. |
| Office Action Summary from U.S. Appl. No. 09/878,789 which was mailed on Oct. 19, 2006. |
| Notice of Allowance from U.S. Appl. No. 09/878,789 which was mailed on Dec. 13, 2007. |
| IEEE Std 802.16e™-2005 and IEEE Std 802.16™-2004/Cor1-2005 (Amendment and Corrigendum to IEEE Std 802.16-2004), “IEEE Standard for Local and metropolitan area networks, Part 16: Air Interface for Fixed and Mobile Broadband Wireless Access Systems, Amendment 2: Physical and Medium Access Control Layers for Combined Fixed and Mobile Operation in Licensed Bands and Corrigendum 1,” Feb. 28, 2006. |
| IEEE Std 802.11a-1999 (R2003) (Supplement to IEEE Std 802.11-1999) [Adopted by ISO/IEC and redesignated as ISO/IEC 8802-11:1999/Amd1:2000(E)], “Supplement to IEEE Standard for Information technology—Telecommunications and information exchange between systems—Local and metropolitan area networks—Specific requirements, Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) specifications, High-speed Physical Layer in the 5 GHz Band,” Reaffirmed Jun. 12, 2003. |
| IEEE Std 802.11b-1999 (R2003) (Supplement to ANSI/IEEE Std 802.11, 1999 Edition) “Supplement to IEEE Standard for Information technology—Telecommunications and information exchange between systems—Local and metropolitan area networks—Specific requirements, Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) specifications, Higher-Speed Physical Layer Extension in the 2.4 GHz Band,” Reaffirmation Jun. 12, 2003, Approved Sep. 16, 1999. |
| IEEE Std 802.11g™-2003, (Amendment to IEEE Std 802.11™, 1999 Edition (Reaff 2003) as amended by IEEE Stds 802.11a™-1999, 802.11b™-1999, 802.11b™-1999/Cor 1-2001, and 802.11d™-2001), “IEEE Standard for Information technology—Telecommunications and information exchange between systems—Local and metropolitan area networks—Specific requirements, Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) specifications, Amendment 4: Further Higher Data Rate Extension in the 2.4 GHz Band,” Jun. 27, 2003. |
| IEEE Std 802.11n™-2009, (Amendment to IEEE Std 802.11™-2007, as amended by IEEE Std 802.11k™-2008, IEEE Std 802.11r™-2008, IEEE Std 802.11y™-2008, and IEEE Std 802.11w™-2009), “IEEE Standard for Information technology—Telecommunications and information exchange between systems—Local and metropolitan area networks—Specific requirements, Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) specifications, Amendment 5: Enhancements for Higher Throughput,” Oct. 29, 2009. |
| IEEE Std 802.16™-2009 (Revision of IEEE Std 802.16-2004), “IEEE Standard for Local and metropolitan area networks, Part 16: Air Interface for Broadband Wireless Access Systems,” May 29, 2009. |
| IEEE Std 802.11 e/D4.3, May 2003 (Draft Supplement to IEEE Std 802.11, 1999 Edition), “Draft Supplement to Standard for Telecommunications and Information Exchange Between Systems—LAN/MAN Specific Requirements—Part 11: Wireless Medium Access Control (MAC) and Physical Layer (PHY) specifications: Medium Access Control (MAC) Enhancements for Quality of Service (QoS),” Copyright 2003. |
| 3GPP TS 36.101 V8.0.0 (Oct. 2007) Technical Specification, “3rd Generation Partnership Project; Technical Specification Group Radio Access Network; Evolved Universal Terrestrial Radio Access (E-UTRA); User Equipment (UE) radio transmission and reception (Release 8),” Copyright 2007. |
| 3GPP TS 36.101 V9.5.0 (Oct. 2010) Technical Specification, 3rd Generation Partnership Project; Technical Specification Group Radio Access Network; Evolved Universal Terrestrial Radio Access (E-UTRA); User Equipment (UE) radio transmission and reception (Release 9), Copyright 2010. |
| Office Action Summary from U.S. Appl. No. 11/880,825 which was mailed on Jul. 16, 2010. |
| Final Office Action Summary from U.S. Appl. No. 11/880,825 which was mailed on Mar. 31, 2011. |
| Chunchang et al, “On the Performance of Eigen Based Beamforming in LTE-Advanced,” Sep. 2009, Personal, Indoor and Mobile Radio Communications (PIMRC), 2009 IEEE 20th International Symposium on. |
| Farrokh Rashid-Farrokhi, “Transmit Beamforming and Power Control for Cellular Wireless Systems,” IEEE Journal on Selected Areas in Communications, vol. 16, No. 8, Oct. 1998. |
| J. H. Chang et al., “Joint Transmitter and Receiver Beamforming for Maximum Capacity in Spatial Division Multiaccess,” Proc. 35th Annual Allerton Conf. on Communication, Control and Computing in Spatial Division Multiaccess, 1997. |
| Office Action from U.S. Appl. No. 13/022,615 which was mailed on Nov. 21, 2011. |
| Office Action from U.S. Appl. No. 13/022,622 which was mailed on Dec. 15, 2011. |
| Office Action from U.S. Appl. No. 13/022,623 which was mailed on Oct. 6, 2011. |
| Notice of Allowance in U.S. Appl. No. 11/880,825 dated Sep. 21, 2012. |
| Notice of Allowance in U.S. Appl. No. 13/022,622 dated Aug. 17, 2012. |
| Notice of Allowance in U.S. Appl. No. 13/022,623 mailed Jul. 12, 2012. |
| Notice of Allowance in U.S. Appl. No. 13/022,615 mailed Apr. 16, 2012. |
| T. Rappaport, B. Woerner, J. Reed, “Wireless Personal Communications: Trends and Challenges,” pp. 69-80, 1994. |
| G. Raleigh et al., “A Blind Adaptive Transmit Antenna Algorithm for Wireless Communication,” in IEEE International Communications Conference on Communications, Jun. 1995. |
| D. Gerlach, A. Paulraj, “Spectrum Reuse Using Transmitting Antenna Arrays with Feedback,” in Proc. International Conference on Acoustics, Speech, and Signal Processing, pp. 97-100, Apr. 1994. |
| D. Gerlach, A. Paulraj, “Adaptive Transmitting Antenna Arrays with Feedback,” in IEEE Signal Processing Letters, vol. 1, No. 10, pp. 150-152, Oct. 1994. |
| D. Gerlach, A. Paulraj, “Adaptive Transmitting Antenna Methods for Multipath Environments,” Global Tele. Conf., 1994. |
| J. Litva, T. Lo, “Digital Beamforming in Wireless Communications,” 1996. |
| F. Rashid-Farrokhi, L. Tassiulas, K. J. Liu, “Joint optimal power control and beamforming in wireless networks using antenna arrays,” IEEE Transactions on Communications, vol. 46, No. 10, pp. 1313-1324, Oct. 1998. |
| G. Raleigh, J. Cioffi, “Spatio-Temporal Coding for Wireless Communications,” IEEE Trans. Comm., Mar. 1998, vol. 46, No. 3, pp. 357-366. |
| G. Raleigh, J. Cioffi, “Spatio-Temporal Coding for Wireless Communications,” Proc. 1996 Global Telecommunications Conf., pp. 1809-1814, Nov. 1996. |
| G. Foschini, M. Gans, “On Limits of Wireless Communication in a Fading Environment When Using Multiple Antennas,” Wireless Personal Comm., vol. 6, No. 3, pp. 311-355, Mar. 1998. |
| T. M. Cover, J. A. Thomas, “Elements of Information Theory,” 1991. |
| K. Boulle, G. Femenias, R. Augsti, “An Overview of Trellis Coded Modulation Research in COST 231,” in IEEE PIMRC '94, pp. 105-109, Sep. 1994. |
| W. Gardner, “Multiplication of Cellular Radio Capacity by Blind Adaptive Spatial Filtering,” Proc. IEEE International Conference on Selected Topics in Wireless Communication, Jun. 1992, pp. 102-106. |
| R.S. Cheng, S. Verdu, “On Limiting Characterizations of Memoryless Multiuser Capacity Regions,” IEEE Transactions on Information Theory, vol. 39, No. 3, May 1993. |
| S. Verdu, “The Capacity Region of the Symbol-Asynchronous Gaussian Multiple-Access Channel,” IEEE Transactions on Information Theory, vol. 35, No. 4, Jul. 1989. |
| B. Suard, G. Xu, H. Liu, T. Kailath, “Uplink Channel Capacity of Space-Division-Multiple-Access Schemes,” IEEE Transactions on Information Theory, vol. 44, No. 4, Jul. 1998. |
| B. Suard, G. Xu, H. Liu, T. Kailath, “Channel Capacity of Spatial Division Multiple Access Schemes,” 1994 Conference Record of the Twenty-Eighth Asilomar Conference on Signals, Systems, and Computers, vol. 2, pp. 1159-1163, 1994. |
| G. Raleigh, V. Jones, “Adaptive Antenna Transmission for Frequency Duplex Digital Wireless Communication,” Proc. IEEE ICC, vol. 2, pp. 641-646, Jun. 1997. |
| G. Raleigh, V. Jones, “Multivariate Modulation and Coding for Wireless Communication,” IEEE Journal on Selected Areas in Communications, vol. 17, No. 5, May 1999. |
| S. Swales, M. Beach, D. Edwards, J. McGeehan, “The performance enhancement of multibeam adaptive base station antennas for cellular land mobile radio systems,” IEEE Trans. Vehic. Tech., vol. VT-39, pp. 56-67, Feb. 1990. |
| P. Chow, J. Tu, J. Cioffi, “Performance Evaluation of a Multichannel Transceiver System for ADSL and VHDSL Services,” IEEE Journal on Selected Areas in Comm., vol. 8, No. 4, Aug. 1991. |
| B. Agee, P. Kelly, D. Gerlach, “The backtalk airlink for full exploitation of spectral and spatial diversity in Wireless Communication Systems,” Proc. Fourth Workshop on Smart Antennas in Wireless Mobile Communications, Jul. 1997. |
| R. Cheng, S. Verdu, “Gaussian Multiaccess Channels with ISI: Capacity Region and Muluser Water-Filling,” IEEE Transactions on Information Theory, vol. 39, No. 3, May 1993. |
| Notice of Allowance in U.S. Appl. No. 11/880,825 mailed Apr. 3, 2012. |
| Office Action Summary in U.S. Appl. No. 13/010,629 dated Aug. 14, 2012. |
| 3rd Generation Partnership Project 2, “Physical Layer Standard for cdma2000 Spread Spectrum Systems,” Release 0, 3GPP2 C.S0002-0-2, Version 1.13, Apr. 24, 2001, Apr. 24, 2001. |
| Office Action Summary from U.S. Appl. No. 13/022,615 dated Mar. 12, 2013. |
| Interview Summary from U.S. Appl. No. 13/022,615 dated Mar. 25, 2013. |
| 3rd Generation Partnership Project 2, “Physical Layer Standard for cdma2000 Spread Spectrum Systems,” Release 0, 3GPP2 C.S0002-0-2, Version 1.13, Apr. 24, 2001. |
| 3rd Generation Partnership Project 2, “Medium Access Control (MAC) Standard for cdma2000 Spread Spectrum Systems,” Release 0—Addendum 2, 3GPP2 C.S0003-0-2, Version 1.0, Apr. 24, 2001. |
| 3rd Generation Partnership Project 2, “Signaling Link Access Control (LAC) Standard for cdma2000 Spread Spectrum Systems—Addendum 2,” 3GPP2 C.S0004-0-2, Version 1.0, Apr. 24, 2001. |
| 3rd Generation Partnership Project 2, “Upper Layer (Layer 3) Signaling Standard for cdma2000 Spread Spectrum Systems,” Release 0 Addendum 2, 3GPP2 C.S0005-0-2, Version 1.0, Apr. 24, 2001. |
| 3rd Generation Partnership Project, Technical Specification Group GERAN, “Digital cellular telecommunications system (Phase 2+); Physical layer on the radio path; General description,” Release 4, 3GPP TS 45.001, V4.0.1, Oct. 2000. |
| 3rd Generation Partnership Project, Technical Specification Group GSM/EDGE “Radio Access Network; Digital cellular telecommunications system (Phase 2+); Multiplexing and multiple access on the radio path,” Release 4, 3GPP TS 45.002, V4.3.0, Apr. 2001. |
| 3rd Generation Partnership Project, Technical Specification Group GERAN, “Channel coding,” Release 4, 3GPP Ts 45.003, V4.0.0, Jan. 2001. |
| 3rd Generation Partnership Project, Technical Specification Group GSM/EDGE Radio Access Network, “Digital cellular telecommunications system (Phase 2+); Modulation,” Release 4, 3GPP TS 45.004, V4.0.0, Apr. 2001. |
| 3rd Generation Partnership Project, Technical Specification Group GSM/EDGE Radio Access Network, “Digital cellular telecommunications system (Phase 2+); Radio transmission and reception,” Release 4, 3GPP TS 45.005, V4.3.0, Apr. 2001. |
| 3rd Generation Partnership Project, Technical Specification Group GSM/EDGE Radio Access Network, “Digital cellular telecommunications system (Phase 2+); Radio subsystem link control,” Release 4, 3GPP TS 45.008, V4.3.0, Apr. 2001. |
| 3rd Generation Partnership Project, Technical Specification Group GERAN, “Link Adaptation,” Release 4, 3GPP TS 45.009, V4.0.0, Jan. 2001. |
| 3rd Generation Partnership Project, Technical Specification Group GSM/EDGE Radio Access Network, “Digital cellular telecommunications system (Phase 2+); Radio subsystem synchronization,” Release 4, 3GPP TS 45.010, V4.0.0, Apr. 2001. |
| 3rd Generation Partnership Project, Technical Specification Group Radio Access Network, “Physical channels and mapping of transport channels onto physical channels (FDD),” Release 199, 3GPP TS 25.211, V3.6.0, Mar. 2001. |
| 3rd Generation Partnership Project, Technical Specification Group Radio Access Network, “Multiplexing and channel coding (FDD),” Release 1999, 3GPP TS 25.212, V3.5.0, Dec. 2000. |
| 3rd Generation Partnership Project, Technical Specification Group Radio Access Network, “Spreading and modulation (FDD),” Release 1999, 3GPP TS 25.213, V3.5.0, Mar. 2001. |
| 3rd Generation Partnership Project, Technical Specification Group Radio Access Network, “Physical layer procedures (FDD),” Release 1999, 3GPP TS 25.214, V3.6.0, Mar. 2001. |
| 3rd Generation Partnership Project, Technical Specification Group Radio Access Network, “Physical channels and mapping of transport channels onto physical channels (TDD),” Release 1999, 3GPP TS 25.221, V3.6.0, Mar. 2001. |
| 3rd Generation Partnership Project, Technical Specification Group Radio Access Network, “Multiplexing and channel coding (TDD),” Release 1999, 3GPP TS 25.222, V3.6.0, Mar. 2001. |
| 3rd Generation Partnership Project, Technical Specification Group Radio Access Network, “Spreading and modulation (TDD),” Release 1999, 3GPP TS 25.223, V3.5.0, Mar. 2001. |
| 3rd Generation Partnership Project, Technical Specification Group Radio Access Network, “Physical Layer Procedures (TDD),” Release 1999, 3GPP TS 25.224, V3.6.0, Mar. 2001. |
| 3rd Generation Partnership Project, Technical Specification Group Radio Access Network, “MAC protocol specification,” Release 1999, 3GPP TS 25.321, V3.7.0, Mar. 2001. |
| 3rd Generation Partnership Project, Technical Specification Group Radio Access Network, “RLC protocol specification,” Release 1999, 3GPP TS 25.322, V3.6.0, Mar. 2001. |
| 3rd Generation Partnership Project, Technical Specification Group Radio Access Network, “Packet Data Convergence Protocol (PDCP) Specification,” Release 1999, 3GPP TS 25.323, V3.4.0, Mar. 2001. |
| 3rd Generation Partnership Project, Technical Specification Group Radio Access Network, RRC Protocol Specification, Release 1999, 3GPP TS 25.331, V3.6.0, Mar. 2001. |
| Number | Date | Country | |
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| 20110255577 A1 | Oct 2011 | US |
| Number | Date | Country | |
|---|---|---|---|
| 60234831 | Sep 2000 | US | |
| 60211462 | Jun 2000 | US |
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| Parent | 11880825 | Jul 2007 | US |
| Child | 13022619 | US |
| Number | Date | Country | |
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| Parent | 09878789 | Jun 2001 | US |
| Child | 11880825 | US |
