PROGRESSIVE CHANNEL STATE INFORMATION

Abstract

Embodiments of system, device, and method configurations for implementing a progressive channel state indicator (CSI) evaluation are disclosed herein. In one example, techniques for increasing the precision of channel feedback information by detecting a best beam index in a progressively scanning grid of beams is deployed in a Long Term Evolution (LTE) network. The use of a progressive CSI technique may be used to improve the robustness of beamforming in network configurations deploying multi-user multiple input multiple output (MU-MIMO) transmission modes.

Description

TECHNICAL FIELD

Embodiments pertain to operations and communications performed by electronic devices in wireless networks. Some embodiments relate to operations for communicating channel feedback information between a mobile device and network equipment in a wide area network such as a cellular phone network.

BACKGROUND

Next generation mobile networks, such as 3GPP Long Term Evolution (LTE)/Long Term Evolution-Advanced (LTE-A) networks, are commonly deployed in a multi-radio environment where a mobile station device, referred to as User Equipment (UE) in LTE/LTE-A, is equipped with multiple radio transceivers. One radio technique used by UEs in LTE/LTE-A networks is Multi-User Multiple-Input and Multiple-Output (MIMO), commonly abbreviated as MU-MIMO, which involves the use of multiple antennas among transmitters and receivers to increase communication throughput and performance. Channel State Information (CSI) plays an important role in the performance of MU-MIMO techniques and the resulting communication throughput. However, the use of MU-MIMO in large antenna arrays, e.g., massive or full-dimension MIMO settings, results in a linear increase in the training and feedback needed to properly handle CSI requirements.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates operation of a progressive channel state information reference symbol scan according to a described example.

FIG. 2 illustrates a mapping of individual beams to reference symbols within a resource element mapping according to a further described example.

FIG. 3A illustrates a beam group configuration for a LTE-compatible channel state information reference symbol scan according to a further described example.

FIG. 3B illustrates a beam group configuration for a high density horizontal channel state information reference symbol scan according to a further described example.

FIG. 4 illustrates a channel state information capture for a beam group according to a further described example.

FIG. 5 illustrates a vertical antenna mapping to channel state information reference symbol ports for an elevation channel state information acquisition according to a further described example.

FIG. 6 illustrates an overall sequence of vertical and horizontal channel state information acquisition and update according to a further described example.

FIG. 7 illustrates a flowchart of a horizontal and vertical CSI acquisition and update technique performed in a wireless network according to a further described example.

FIG. 8 illustrates a flowchart of a detailed progressive CSI technique performed by a wireless network according to a further described example.

FIG. 9 illustrates an example mobile client device on which the configurations and techniques described herein may be deployed.

FIG. 10 illustrates an example computer system that may be used as a computing platform for the computing or networking devices described herein.

DETAILED DESCRIPTION

The following description and the drawings sufficiently illustrate specific embodiments to enable those skilled in the art to practice them. Other embodiments may incorporate structural, logical, electrical, process, and other changes. Portions and features of some embodiments may be included in, or substituted for, those of other embodiments. Embodiments set forth in the claims encompass all available equivalents of those claims.

The following describes example system and device implementations and techniques to enhance the use of multi-user MIMO through the use of a progressive CSI technique. A major challenge of MU-MIMO is obtaining access to high quality CSI at the transmitter. This requirement comes at considerable training and feedback costs for higher dimension MIMO in frequency-division duplexing (FDD) cellular systems. The following disclosure includes example techniques to reduce feedback growth and improve beamforming performance of MU-MIMO based on enhanced progressive CSI techniques.

CSI plays a significant role in the performance of MU-MIMO. Availability of error-free and high resolution CSI at the transmitter drastically increases system throughput. The acquisition of CSI, however, conflicts squarely with the need to minimize signaling overhead and feedback traffic in the FDD systems. With large antenna arrays, i.e., massive or full-dimension MIMO, a linear increase in the training and feedback quickly becomes excessive. In the case of two-dimensional antenna configurations, beamforming in the elevation domain further complicates the CSI requirements.

In addition, performance of MU-MIMO is also limited by the specific choice of the codebooks needed for CSI quantization. Codebooks matched poorly to the instantaneous user channel will not represent channel characteristics with acceptable quality. The following provides detailed implementations that avoid the use of quantization based on pre-set fixed code books for both horizontal and vertical domains.

The following also provides detailed implementations that incrementally increase the precision of channel feedback information by detecting the best beam index in a progressively scanning grid of beams. The feedback overhead growth will be sub-linear on average as CSI updates will be sent back to the evolved NodeB (eNodeB) only when a stronger beam is detected. At the end of the progressive CSI period, high precision CSI is obtained with effectively smaller number of transmitted CSI bits. In the case of 2-D antenna arrays (e.g., 4×4 or 8×8 antenna arrays) covering users in a 3-dimensional space, one further implementation operates to obtain analog vertical CSI at the beginning of a relatively long period with differential update throughout the period. This incremental CSI scheme is justified due to the fairly static nature of vertical channel characteristics.

The following opportunistic and incremental CSI precision enhancements may be used to reduce feedback on the uplink. The progressive scanning may provide flexible periodicity to match cell traffic and channel conditions, similar to the CSI-RS repetition pattern implemented in LTE Release 10. Large antenna arrays are also supported in the following techniques with use of the existing antenna port structure of current specifications. Further, as explained in the following techniques, the robustness of beamforming may be significantly improved by a) weaning precoding weights from pre-set codebooks, and b) maintaining zero-forcing as an option to remove intra-user interference.

FIG. 1 provides an illustration of an example operating environment 100 for progressive CSI operations according to one example. In the operating environment 100, a systematic scan of the users' mobile devices 110, 112 (e.g., user equipment) by a set of fixed beams is performed. Specifically, FIG. 1 illustrates the progressive scanning and evaluation of a series of CSI group transmissions 104, 106, 108 among a distributed area. The following techniques disclose mechanisms to conduct such distributed progressive scanning and evaluation in both one-dimensional (1-D) and two-dimensional (2-D) antenna arrays.

To cover both 1-D and 2-D antenna arrays, a determination is performed for both horizontal and vertical beamforming weights for a particular UE. Using a separable horizontal/vertical channel model, the final transmitted signal vector for the k-th UE is described by x_k=(W_h^(k)*q_k)W_v^(k) where W_h^(k), W_v^(k) are the horizontal and vertical precoding weights, respectively, q_k is the data vector intended for the user, and *, denote matrix and Kronecker multiplications, respectively.

Reference Signals for Progressive Scan in the Horizontal Domain

Given larger angular spread in the horizontal domain, progressive scanning can be applied in the horizontal domain. Following general principles of random beamforming, a fixed set of orthonormal precoding vectors and distinct groups of rotated beams may be used to incrementally refine the CSI at the transmitter.

First, an orthonormal set, {φ_0,i}_i=0^Mh is generated where M_h corresponds to the number of beams. One way, though not exclusively, is the use of a size M_h Discrete Fourier Transform (DFT) basis. Next, multiple beam groups are created from the base set, {φ₀}, {φ₁}, . . . , {φ_Gh-1} where G_h is the total number of beam groups in the horizontal domain. There are different ways of generating G_h beam groups; one way is to create a large M_hG_h-size DFT basis and to decimate it regularly to arrive at the groups. Another way is to use the same M_h-size DFT and apply a fixed Δ phase shift successively.

The use of a fixed Δ phase shift 134 is shown in FIG. 1, with the distribution of progressive beam group transmissions 104, 106, 108 that are transmitted from an eNodeB 102 at the respective phase shifts as illustrated. The precoding vectors of base zero forms a grid of beams at an initial Angle of Departure (AoD) of ψ₀ 132. The successive groups will increment AoD by Δ phase shift 134, e.g., at positions φ₀₀ 114, φ₀₁ 116, φ₀₂ 118, φ₀₃ 120, φ₀₄ 122, φ₀₅ 124, φ₀₆ 126, φ₀₇ 128.

References signals accordingly may be distributed with use of the progressive beam group transmissions 104, 106, 108. The reference signal construction may be established using the following procedure.

Channel state reference signals (CSI-RS) may be used to map each beam to the respective CSI-RS port. As defined in LTE Release 10, up to 8 antenna ports can be supported in a given subframe. This is illustrated in the resource elements block 200 of FIG. 2, with mapping of individual beams to reference symbols R₁-R₈ for the case of 8 CSI-RS ports. In the example of FIG. 2, R₁-R₈ are transmitted among resource elements 210, 212, 214, and 216 (with “C” in resource elements 210, 212, 214, and 216 denoting code division multiplexing (CDM) between two symbols) within slot 202 (the first of two slots of a subframe). Each R_i resource element is transmitted in the direction of one of the beams in a given beam group. (No reference signals are transmitted within subsequent slot 204, the second of two slots of a subframe).

This specific mapping between CSI-RS/antenna port and the beam is known at both UE and eNodeB nodes. The option for semi-static configuration of this mapping can be provided by some form of higher-layer signaling. As a result, based on a specific measure of signal strength (such as signal-to-noise ratio (SNR), signal-to-interference-plus-noise ratio (SINR), or related metrics) in a given CSI-RS resource element, the UE can measure the receive quality of that particular beam.

At the beginning of a scan procedure, the grid of beams produced by the φ₀ group (e.g., group transmission 104 depicted in FIG. 1) and at the leading ψ₀ AoD (e.g., ψ₀ 132 depicted in FIG. 1) are mapped to the 8 CSI-RS ports. After φ₀ period, the second beam group, φ₁ (e.g., group transmission 106 depicted in FIG. 1) will be mapped to the 8 ports. After transmitting CSI-RS with the last beam group of φ_Gh-1 having the leading AoD of ψ₀+(G_h−1)Δ, the scan will rotate back to the initial position and the first beam group.

In a progressive scan, the CSI-RS of the individual beam groups may be transmitted in different density modes. Two density modes described in the following paragraphs include: a LTE-Compatible Mode, adapted for the compatibility of the level used in LTE Release 10; and a High Density Mode, configured to support higher density reference signal configurations (at a potential cost to decoding performance of legacy UEs).

LTE-Compatible Mode: FIG. 3A depicts an LTE Release 10 compatible CSI-RS beam group configuration 300, deployed for a full progressive CSI period 302, with spacing between subframes for respective sub-periods (e.g., spacing within beam group CSI sub-period 304). Each CSI-RS group can be inserted with the periodicity of 5, 10, 20, 40, or 80 subframes, as specified in LTE Release 10. That is, between two consecutive beam groups (sent out in subframes 306 and 308, for example, depicted in FIG. 3A), there will be a gap of multiple subframes with the corresponding CSI-RS inserted only once, as illustrated in FIG. 3A. During one full progressive CSI period 302, all AoD angles are scanned and the corresponding CSI-RSs are transmitted to the UEs. CSI-RS structure in this mode is transparent to the Release 10 UE and is more appropriate for semi-static low-mobility deployments. System configuration parameter will be the same as LTE Release 10, that is, the periodicity of CSI-RS. There is no increased degradation to the decoding performance of the legacy UEs because the density and frequency of CSI-RS are maintained at the same level as Release 10.

High Density Mode: FIG. 3B depicts a High Density horizontal CSI-RS beam group configuration 350, deployed for a horizontal CSI progressive scan period 352. To support higher density reference signal configurations, multiple CSI-RS may be inserted within one LTE Release 10 period (e.g., the horizontal CSI progressive scan period 352, such as a frame) to exhaust all beam groups. For example, FIG. 3B shows the case of 5 beam groups with lead AoD angles of 0, Δ, 2Δ, 3Δ, and 4Δ. The corresponding group CSI-RS is inserted in each subframe and the pattern repeats (either in direct or reverse order) every 5n,n=1,2,4,8,16 subframes. Due to higher frequency of CSI-RS insertion in this mode, a degradation in the decoding performance of legacy UEs should be anticipated.

Feedback Scheme for Progressive CSI

One of the objectives of the progressive CSI technique discussed herein is to enhance the precision of CSI at the transmitter without resorting to impractically large codebook sizes. To this end, the feedback parameters are calculated by the k-th UE with channel H_k, as shown in FIG. 4 for K active users in the cell. As described in more detail below, FIG. 4 illustrates a CSI capture deployed with beams transmitted to the respective cell users (e.g., UE 1 412, UE 2 414, UE k 416, and UE K 418).

CQI (Channel Quality Indication): As each CSI represents a specific beam, a respective UE can calculate the strength of that beam by measuring SINR at the corresponding CSI-RS. Because the UE is aware of the pre-set mapping sequence between the beam index and the CSI-RS, it can identify which beam has the best SINR quality among the mapped beams, e.g., the eight mapped beams, in the subframe where CSI is present. At the beginning of the operation, the Modulation and Coding Scheme (MCS) index corresponding to CQI₀=max_{i=0, . . . , 7}|H_kφ_0,i|², will be sent to the eNodeB (e.g., eNodeB 402). In the subsequent CSI-RS subframes, CQI_i, i=1, . . . , G_h−1 will be calculated in a similar manner but at the i-th CSI-RS sub-period, CQI value is forwarded only if CQI_i>CQI*_k, where CQI*_k is the best transmitted CQI to this point for the k-th UE and the CQI*_k=CQI_i update will take place at the eNodeB. If CQI_i≦CQI*_k no CQI feedback will be sent back by the UE as it also tracks the evolution of CQI.

Pre-coding Matrix Indicator (PMI): The index of the best beam identified in the above CQI feedback procedure is also progressively forwarded to the eNodeB (e.g., eNodeB 402), that is, at the end of progressive CSI period, eNodeB can identify

${\mathrm{PMI}}_k^{*}={\mathrm{argmax}}_{\underset{i=0,\dots ,G_h-1}{j=0,\dots ,8,}}{H_k{\phi }_{j,i}}^2$

for the k-th UE.

FIG. 4 illustrates a CSI capture technique for the respective cell users (e.g., UE 1 412, UE 2 414, UE k 416, and UE K 418) for one beam group including 4 beams (e.g., beams 420, 422, 424, 426). This is produced from the progressive scanning of beams communicated from groups of beams 404, 406, 408, 410, 412 transmitted by the transmission antennas of an eNodeB 402. As a result, after a full progressive CSI period, all the beams in each beam group are measured to allow the strongest beam to be identified for each active UE in the cell. At the end of this period, a much more refined CSI is obtained at the transmitter given the feedback from multiplicity of beam groups separated by Δ AoD.

For example, with 5 groups and 8 beams in each group, PMI*_K has an effective precision of 5*log₂ 8=15 bits. The key idea behind feedback reduction is that the effective 15-bit PMI is generated while transmitting much less than 15 bits on average. Actual uplink feedback transmission takes place only if the new group CQI is stronger than the current maximum CQI.

CSI Update Sequence

The discussion above explains how progressive CSI can lead to high precision PMI and CQI within the progressive scanning period. At the start of the operation, for a length of G_h*L_SF subframes, an eNodeB can be capturing and evaluating CSI from all of the beam groups where L_SF denotes the number of subframes between two consecutive CSI-RS groups (for LTE-compatible mode, it can be any of the 5, 10, 20, 40, and 80 subframes and for high-density mode, it is 1). After this fixed initial delay, after the arrival of each new CSI-RS subframe, an eNodeB can update its evolving CQI and PMI metrics and make an immediate decision.

In other words, with each new CSI-RS group, either the previous best beam candidate (PMI*, CQI*) set is maintained or gets updated by the new stronger beam candidate. Therefore, a transition to a new progressive CSI period does not have to reset the entire history of CSI progression and to start anew. This means aside from the first few subframes allocated to the build-up of valid CSI at the eNodeB, progressive CSI does not impose a latency constraint on the system operation.

It should be added that, for various reasons, CSI information at the eNodeB may become outdated and potentially invalid. As a remedy, a reset operation at a much longer time scale than normal update period of L_SF subframes can be programmed to restart the sequence of progressive beam scanning and CSI tracking.

Horizontal MU-MIMO Beamforming Techniques

At the conclusion of the progressive CSI feedback, the eNodeB collects PMI*={PMI*₁, PMI*₂, . . . , PMI*_K} which is a set that should indicate the best beam indices for all the active users. This set has a one-to-one correspondence with φ*={φ*₁, φ*₂, . . . , φ*_K} where W_h^(k)=φ_k* is the best horizontal precoding vector for the k-th user assuming it is the only user to be served. For MU-MIMO techniques, an eNodeB can adopt different beamforming schemes including maximal-ratio transmission, random beamforming, etc. This also includes zero-forcing to null inter-user interference which is achieved by inverting the matrix of aggregated user channels after a user selection scheme narrows down the specific users in the MU-MIMO user set.

Vertical Beamforming Techniques. To accommodate 2-D large antenna arrays and when users are distributed in 3-D space, proper vertical beamforming can steer the transmission to the intended users in the elevation domain. With a much smaller vertical angle spread, large number of antennas in each column of the 2D configuration should produce adequately narrow beams to differentiate the users.

To circumvent this difficulty and also considering relatively smaller channel variations in the vertical domain, a different technique may be used to acquire vertical CSI from the previously described progressive scan technique adapted for horizontal CSI. With the separable precoding model denoted by x_k=(W_h^(k)*q_k)W_v^(k), elevation precoding weights are obtained independently from those of the horizontal domain.

To this end, FIG. 5 illustrates a vertical antenna mapping of respective antennas to CSI-RS ports. This mapping is used to establish an elevation CSI acquisition, illustrated by an example set of antenna arrays 500, 510 including antennas (e.g., antennas 502, 512) respectively arranged in matrices. Any one of the antenna columns (e.g., columns 504, 514) may be mapped to the CSI-RS ports, as shown in FIG. 5 for 8×8 and 4×4 antenna arrays 500, 510.

To avoid codebook-based CSI quantization, explicit analog feedback may be used from processing the CSI in an initial vertical CSI acquisition subframe or a small number of subframes. This is illustrated with the vertical CSI acquisition period 602 depicted in FIG. 6.

FIG. 6 provides an illustration of an overall coordinated sequence 600 of horizontal and vertical CSI acquisition and update. The sequence 600 includes an initial vertical CSI acquisition period 602, followed by an initial horizontal CSI acquisition period 604. This is followed by a horizontal/vertical CSI restart period 606. Within the horizontal/vertical CSI restart period 606, vertical differential CSI periods 606, 610 may be conducted, with the horizontal progressive CSI period 608 occurring after the first vertical differential CSI period 606.

Even though operation of the vertical CSI acquisition period 602 incurs a larger overhead than traditional LTE implicit, quantized feedback, the vertical CSI acquisition period 602 is used at the start of the operation to bootstrap the beamforming process. After this initial phase, only limited and sporadic differential CSI feedback for the vertical channel (from vertical differential CSI periods 606, 610) is forwarded to the eNodeB.

The exact nature and mechanism for the analog feedback and subsequent differential feedbacks may vary based on requirements, standards, and implementation considerations. The overall vertical CSI feedback traffic typically will remain minimal given the stationary nature of the vertical channel To prevent any error propagation, however, the vertical CSI restart period may be synchronized with the horizontal progressive CSI procedure.

FIG. 7 illustrates a flowchart 700 of an example horizontal and vertical CSI acquisition and update technique that is implemented or performed by a wireless network collecting CSI information for use in beamforming with a two-dimensional antenna array according to a further described example. As shown, the operations parallel those depicted in FIG. 6, with the performance of an initial CSI acquisition (operation 702) followed by the performance of a CSI update (operation 708).

In more detail, the performance of an initial CSI acquisition (operation 702) includes the performance of an initial vertical CSI acquisition (operation 704) followed by the performance of an initial horizontal CSI acquisition (operation 706). Vertical CSI acquisition and update is used to establish proper vertical beamforming characteristics that steer the transmission to the intended users in the vertical (elevation) domain, whereas horizontal CSI is used to establish proper beamforming characteristics that also steer the transmission to intended users in the horizontal domain. The initial vertical CSI acquisition (operation 704) obtains feedback from processing the CSI in an initial vertical CSI acquisition subframe or a small number of subframes; the initial horizontal CSI acquisition (operation 706) obtains feedback from a progressive horizontal CSI scanning operation conducted in a LTE-compatible mode or a high density mode.

The performance of CSI update (operation 708) is conducted subsequent to the initial CSI acquisition, and includes the performance of a vertical differential CSI update (operation 710) and the performance of a horizontal differential CSI update (operation 712). The CSI updates are performed after a certain delay and enable evaluation of CQI and PMI metrics. The performance of a vertical differential CSI update (operation 710) may include only a limited and sporadic differential evaluation; whereas the performance of a horizontal differential CSI update (operation 712) may include a fuller progressive CSI evaluation similar to the initial horizontal CSI acquisition.

FIG. 8 illustrates a flowchart 800 of a more detailed progressive CSI technique that is implemented or performed by an eNodeB or other hardware of a wireless network according to a further described example. The flowchart 800 includes operations for progressively scanning with a grid of beams (operation 802). This grid of beams may be customized for performing vertical scanning within an initial or update scanning period (operation 804), followed by performing horizontal scanning within the initial or update scanning period (operation 806). The eNodeB is configured to receive an indication of the best beam index of progressively scanning the network, using best beam index received from respective UEs (operation 808). Based on this best beam index and any other relevant factors, the eNodeB is configured to determine the highest CSI for the respective UEs (operation 810). Subsequent beamforming operations from the eNodeB are then adapted to use the highest CSI for the respective UEs (operation 812).

Although the preceding examples of wireless network connections were provided with specific reference to 3GPP LTE/LTE-A, it will be understood that the techniques described herein may be applied to or used in conjunction with a variety of other WWAN, WLAN, and WPAN protocols and standards. These standards include, but are not limited to, other standards from 3GPP (e.g., HSPA+, UMTS), IEEE 802.11 (e.g., 802.11a/b/g/n/ac), IEEE 802.16 (e.g., 802.16p), or Bluetooth (e.g., Bluetooth 4.0, or like standards defined by the Bluetooth Special Interest Group) standards families. Other applicable network configurations may be included within the scope of the presently described communication networks. It will be understood that communications on such communication networks may be facilitated using any number of personal area networks, LANs, and WANs, using any combination of wired or wireless transmission mediums.

The embodiments described above may be implemented in one or a combination of hardware, firmware, and software. Various methods or techniques, or certain aspects or portions thereof, may take the form of program code (i.e., instructions) embodied in tangible media, such as flash memory, hard drives, portable storage devices, read-only memory (ROM), random-access memory (RAM), semiconductor memory devices (e.g., Electrically Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM)), magnetic disk storage media, optical storage media, and any other machine-readable storage medium or storage device wherein, when the program code is loaded into and executed by a machine, such as a computer or networking device, the machine becomes an apparatus for practicing the various techniques.

A machine-readable storage medium or other storage device may include any non-transitory mechanism for storing information in a form readable by a machine (e.g., a computer). In the case of program code executing on programmable computers, the computing device may include a processor, a storage medium readable by the processor (including volatile and non-volatile memory and/or storage elements), at least one input device, and at least one output device. One or more programs that may implement or utilize the various techniques described herein may use an application programming interface (API), reusable controls, and the like. Such programs may be implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the program(s) may be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language, and combined with hardware implementations.

FIG. 9 provides an example illustration of a mobile device 900, such as a user equipment (UE), a mobile station (MS), a mobile wireless device, a mobile communication device, a tablet, a handset, or other type of mobile wireless computing device. The mobile device 900 may include one or more antennas 908 within housing 902 that are configured to communicate with a hotspot, base station (BS), an evolved NodeB (eNodeB), or other type of WLAN or WWAN access point. The mobile device may be configured to communicate using multiple wireless communication standards, including standards selected from 3GPP LTE/LTE-A, WiMAX, High Speed Packet Access (HSPA), Bluetooth, and Wi-Fi standard definitions. The mobile device 900 may communicate using separate antennas for each wireless communication standard or shared antennas for multiple wireless communication standards. The mobile device 900 may communicate in a WLAN, a WPAN, and/or a WWAN.

FIG. 9 also provides an illustration of a microphone 920 and one or more speakers 912 that may be used for audio input and output from the mobile device 900. A display screen 904 may be a liquid crystal display (LCD) screen, or other type of display screen such as an organic light emitting diode (OLED) display. The display screen 904 may be configured as a touch screen. The touch screen may use capacitive, resistive, or another type of touch screen technology. An application processor 914 and a graphics processor 918 may be coupled to internal memory 916 to provide processing and display capabilities. A non-volatile memory port 910 may also be used to provide data input/output options to a user. The non-volatile memory port 910 may also be used to expand the memory capabilities of the mobile device 900. A keyboard 906 may be integrated with the mobile device 900 or wirelessly connected to the mobile device 900 to provide additional user input. A virtual keyboard may also be provided using the touch screen. A camera 922 located on the front (display screen) side or the rear side of the mobile device 900 may also be integrated into the housing 902 of the mobile device 900.

FIG. 10 is a block diagram illustrating an example computer system machine upon which any one or more of the methodologies herein discussed may be run. Computer system machine 1000 may be embodied as a mobile device, computing system, eNodeB, servers, or any other computing platform described or referred to herein. In alternative embodiments, the machine operates as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, the machine may operate in the capacity of either a server or a client machine in server-client network environments, or it may act as a peer machine in peer-to-peer (or distributed) network environments. The machine may be a personal computer (PC) that may or may not be portable (e.g., a notebook or a netbook), a tablet, a set-top box (STB), a gaming console, a Personal Digital Assistant (PDA), a mobile telephone or smartphone, a web appliance, a network router, switch or bridge, or any machine capable of executing instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein.

Example computer system machine 1000 includes a processor 1002 (e.g., a central processing unit (CPU), a graphics processing unit (GPU) or both), a main memory 1004 and a static memory 1006, which communicate with each other via an interconnect 1008 (e.g., a link, a bus, etc.). The computer system machine 1000 may further include a video display unit 1010, an alphanumeric input device 1012 (e.g., a keyboard), and a user interface (UI) navigation device 1014 (e.g., a mouse). In one embodiment, the video display unit 1010, input device 1012 and UI navigation device 1014 are a touch screen display. The computer system machine 1000 may additionally include a storage device 1016 (e.g., a drive unit), a signal generation device 1018 (e.g., a speaker), an output controller 1032, a power management controller 1034, and a network interface device 1020 (which may include or operably communicate with one or more antennas 1030, transceivers, or other wireless communications hardware), and one or more sensors 1028, such as a Global Positioning Sensor (GPS) sensor, compass, location sensor, accelerometer, or other sensor.

The storage device 1016 includes a machine-readable medium 1022 on which is stored one or more sets of data structures and instructions 1024 (e.g., software) embodying or utilized by any one or more of the methodologies or functions described herein. The instructions 1024 may also reside, completely or at least partially, within the main memory 1004, static memory 1006, and/or within the processor 1002 during execution thereof by the computer system machine 1000, with the main memory 1004, static memory 1006, and the processor 1002 also constituting machine-readable media.

While the machine-readable medium 1022 is illustrated in an example embodiment to be a single medium, the term “machine-readable medium” may include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more instructions 1024. The term “machine-readable medium” shall also be taken to include any tangible medium that is capable of storing, encoding or carrying instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present disclosure or that is capable of storing, encoding or carrying data structures utilized by or associated with such instructions.

The instructions 1024 may further be transmitted or received over a communications network 1026 using a transmission medium via the network interface device 1020 utilizing any one of a number of well-known transfer protocols (e.g., HTTP). The term “transmission medium” shall be taken to include any intangible medium that is capable of storing, encoding, or carrying instructions for execution by the machine, and includes digital or analog communications signals or other intangible medium to facilitate communication of such software.

It should be understood that the functional units or capabilities described in this specification may have been referred to or labeled as components or modules, in order to more particularly emphasize their implementation independence. For example, a component or module may be implemented as a hardware circuit comprising custom very-large-scale integration (VLSI) circuits or gate arrays, off-the-shelf semiconductors such as logic chips, transistors, or other discrete components. A component or module may also be implemented in programmable hardware devices such as field programmable gate arrays, programmable array logic, programmable logic devices, or the like. Components or modules may also be implemented in software for execution by various types of processors. An identified component or module of executable code may, for instance, comprise one or more physical or logical blocks of computer instructions, which may, for instance, be organized as an object, procedure, or function. Nevertheless, the executables of an identified component or module need not be physically located together, but may comprise disparate instructions stored in different locations which, when joined logically together, comprise the component or module and achieve the stated purpose for the component or module.

Indeed, a component or module of executable code may be a single instruction, or many instructions, and may even be distributed over several different code segments, among different programs, and across several memory devices. Similarly, operational data may be identified and illustrated herein within components or modules, and may be embodied in any suitable form and organized within any suitable type of data structure. The operational data may be collected as a single data set, or may be distributed over different locations including over different storage devices, and may exist, at least partially, merely as electronic signals on a system or network. The components or modules may be passive or active, including agents operable to perform desired functions.

Additional examples of the presently described method, system, and device embodiments include the following, non-limiting configurations. Each of the following non-limiting examples may stand on its own, or may be combined in any permutation or combination with any one or more of the other examples provided below or throughout the present disclosure.

Example 1 includes the subject matter embodied by a method that is performed by a device (e.g., an eNodeB, or other wireless telecommunications device) for conducting a progressive channel state indicator (CSI) operation, the method comprising: scanning with a grid of beams, the grid of beams distributed by beamforming in a plurality of beam groups, wherein the grid of beams transmit respective reference signals among a distributed area; and determining, for a particular User Equipment (UE), a highest CSI in the grid of beams from progressively evaluating the respective CSI values produced from scanning with the grid of beams, the determining including: receiving, from the UE, an indication of a best beam index in response to a strongest beam received at the UE from the grid of beams; and selecting the highest CSI in the grid of beams for the particular UE based on the best beam index.

In Example 2, the subject matter of Example 1 can optionally include performing subsequent beamforming operations from the eNodeB to the particular UE, using a result indicated by the highest CSI, wherein the subsequent beamforming operations are used in a multi-user multiple input multiple output (MU-MIMO) transmission mode.

In Example 3, the subject matter of one or any combination of Examples 1-2 can optionally include scanning with a grid of beams including performing horizontal scanning within a progressive scanning period, wherein the grid of beams is configured to transmit the respective reference signals among a horizontal domain for a coverage area of the eNodeB.

In Example 4, the subject matter of one or any combination of Examples 1-3 can optionally include scanning with a grid of beams further including performing vertical scanning within the progressive scanning period, wherein the grid of beams is configured to transmit the respective reference signals among an elevation domain for the coverage area of the eNodeB.

In Example 5, the subject matter of one or any combination of Examples 1-4 can optionally include performing horizontal scanning within a progressive scanning period including conducting a horizontal scan for each beam group CSI sub-period in the plurality of beam groups; wherein sets of CSI reference signals provided in each beam group CSI sub-period are spaced among the plurality of beam groups with a periodicity of a plurality of subframes.

In Example 6, the subject matter of one or any combination of Examples 1-5 can optionally include the periodicity of the plurality of subframes being based on a period of: 5, 10, 20, 40, or 80 subframes.

In Example 7, the subject matter of one or any combination of Examples 1-6 can optionally include performing horizontal scanning within a progressive scanning period including conducting a high-density horizontal scan, wherein multiple CSI reference signals are included within one frame.

In Example 8, the subject matter of one or any combination of Examples 1-7 can optionally include a pattern of the high-density horizontal scan repeating in direct or reverse order every m·n subframes, where m=a number of the plurality of beam groups and n=2, 4, 8, or 16.

In Example 9, the subject matter of one or any combination of Examples 1-8 can optionally include feedback parameters used with conducting the scanning being calculated by a k-th UE connected to the eNodeB, the k-th UE providing the feedback parameters on channel H_k for transmission to the eNodeB in a feedback procedure.

In Example 10, the subject matter of one or any combination of Examples 1-9 can optionally include the best beam index being identified in the feedback procedure and progressively forwarded to the eNodeB; wherein, at an end of a period for scanning with the grid of beams, the eNodeB is adapted to identify a Pre-coding Matrix Indicator PMI*_k for the k-th UE, wherein

${\mathrm{PMI}}_k^{*}={\mathrm{argmax}}_{\underset{i=0,\dots ,G_h-1}{j=0,\dots ,8,}}{H_k{\phi }_{j,i}}^2.$

In Example 11, the subject matter of one or any combination of Examples 1-10 can optionally include a respective UE calculating the strength of a particular beam by measuring signal-to-interference-plus-noise ratio (SINR) with a corresponding CSI reference signal; wherein, at the beginning of a feedback procedure, the Modulation and Coding Scheme (MCS) index corresponding to a Channel Quality Indication CQI₀=max_{i=0, . . . , 7}|H_kφ_0,i|² is provided to the eNodeB.

Example 12 can include, or can optionally be combined with all or portions of the subject matter of one or any combination of Examples 1-11 to include the subject matter embodied by a wireless communication device such as an evolved NodeB (eNodeB), comprising: circuitry comprising circuitry arranged to perform progressive channel state information (CSI) acquisition with operations to: perform a vertical CSI acquisition for an elevation domain; perform a horizontal CSI acquisition by scanning a horizontal domain with a grid of beams, wherein the scanning is customized to the elevation domain; collect progressive CSI feedback from the horizontal CSI acquisition, wherein the eNodeB determines a set of Pre-coding Matrix Indicators PMI*={PMI*₁, PMI*₂, . . . , PMI*_K}, wherein the set of PMI* is a set indicating best beam indices for active UEs connected to the eNodeB; wherein the PMI* is used to adapt different beamforming schemes for subsequent transmissions from the eNodeB to the active UEs.

In Example 13, the subject matter of Example 12 can optionally include the circuitry arranged to perform a determination of a PMI value for a particular User Equipment (UE), in the set of PMI* by: determining a highest CSI in the grid of beams in response to scanning with the grid of beams; receiving, from the particular UE, an indication of a best beam index in response to a strongest beam received at the UE from the grid of beams; and selecting the highest CSI in the grid of beams for the particular UE based on the best beam index.

In Example 14, the subject matter of one or any combination of Examples 12-13 can optionally include the beamforming schemes for subsequent transmissions being used in a multi-user multiple input multiple output (MU-MIMO) transmission mode.

In Example 15, the subject matter of one or any combination of Examples 12-14 can optionally include the operations to perform the horizontal CSI acquisition by scanning including conducting a horizontal scan for each beam group CSI sub-period in a plurality of beam groups; wherein sets of CSI reference signals provided in each beam group CSI sub-period are spaced among the plurality of beam groups with a periodicity of a plurality of subframes.

In Example 16, the subject matter of one or any combination of Examples 12-15 can optionally include the periodicity of the plurality of subframes being directed to a period of: 5, 10, 20, 40, or 80 subframes.

In Example 17, the subject matter of one or any combination of Examples 12-16 can optionally include the operations to perform the horizontal CSI acquisition by scanning including conducting a high-density horizontal scan, wherein multiple CSI reference signals are included within one frame, wherein a pattern of the high-density horizontal scan repeats in direct or reverse order every m·n subframes, where m=a number of beam groups and n=2, 4, 8, or 16.

In Example 18, the subject matter of one or any combination of Examples 12-17 can optionally include feedback parameters for scanning being calculated by a k-th UE connected to the eNodeB with channel H_k for transmission to the eNodeB in a feedback procedure; wherein the best beam index is identified in the feedback procedure and is progressively forwarded to the eNodeB; wherein, at the end of a period for scanning with the grid of beams, the eNodeB is adapted to identify PMI*_k for the k-th UE, wherein

${\mathrm{PMI}}_k^{*}={\mathrm{argmax}}_{\underset{i=0,\dots ,G_h-1}{j=0,\dots ,8,}}{H_k{\phi }_{j,i}}^2.$

Example 19 can include, or can optionally be combined with all or portions of the subject matter of one or any combination of Examples 1-18 to include the subject matter embodied by a method performed by a user equipment (UE) comprising: multiple antennas arranged to receive transmissions from an evolved NodeB (eNodeB), the eNodeB operating in accordance with a standard from a 3GPP Long Term Evolution (LTE) standards family; multiple transceivers operably coupled to the multiple antennas and arranged to receive and transmit wireless communications from the eNodeB, the wireless communications including transmissions received from the eNodeB from a channel state information (CSI) scan performed on a vertical domain and on a horizontal domain; and processing circuitry arranged to process the transmissions received from the eNodeB from the channel state information (CSI) scan and provide progressive CSI feedback in response to the transmissions received from the eNodeB from the progressive channel state information (CSI) scan, wherein an indication of a best beam index is progressively provided in the CSI feedback in response to a determination of a strongest beam received at the UE from the CSI scan.

In Example 20, the subject matter of Example 19 can optionally include the processing circuitry being further arranged to transmit, to the eNodeB, an indication of a best beam index in response to the strongest beam received at the UE from a grid of beams of the CSI scan; wherein the eNodeB operates to determine a highest CSI in the grid of beams for the UE based on the best beam index.

In Example 21, the subject matter of one or any combination of Examples 19-20 can optionally include the CSI scan performed on the horizontal domain including a horizontal scan for each beam group CSI sub-period in a plurality of beam groups; wherein sets of CSI reference signals provided in each beam group CSI sub-period are spaced among the plurality of beam groups with a periodicity of a plurality of subframes, wherein the periodicity of a plurality of subframes is directed to a period of: 5, 10, 20, 40, or 80 subframes.

In Example 22, the subject matter of one or any combination of Examples 19-21 can optionally include the CSI scan performed on the horizontal domain including a high-density horizontal scan, wherein multiple CSI reference signals are inserted within one frame.

In Example 23, the subject matter of one or any combination of Examples 19-22 can optionally include a pattern of the high-density horizontal scan that repeats in direct or reverse order every m·n subframes, where m=a number of beam groups and n=2, 4, 8, or 16.

In Example 24, the subject matter of one or any combination of Examples 19-23 can optionally include feedback parameters for the scanning being calculated by the UE for transmission to the eNodeB in a feedback procedure, wherein the best beam index is identified in the feedback procedure and is progressively forwarded to the eNodeB.

The Abstract is provided to allow the reader to ascertain the nature and gist of the technical disclosure. It is submitted with the understanding that it will not be used to limit or interpret the scope or meaning of the claims. The following claims are hereby incorporated into the detailed description, with each claim standing on its own as a separate embodiment.

Claims

1. A method performed by an evolved Node B (eNodeB) for conducting a progressive channel state indicator (CSI) operation, the method comprising: scanning with a grid of beams, the grid of beams distributed by beamforming in a plurality of beam groups, wherein the grid of beams transmit respective reference signals among a distributed area; anddetermining, for a particular User Equipment (UE), a highest CSI in the grid of beams from progressively evaluating the respective CSI values produced from scanning with the grid of beams, the determining including: receiving, from the UE, an indication of a best beam index in response to a strongest beam received at the UE from the grid of beams; andselecting the highest CSI in the grid of beams for the particular UE based on the best beam index.

2. The method of claim 1, further comprising: performing subsequent beamforming operations from the eNodeB to the particular UE, using a result indicated by the highest CSI, wherein the subsequent beamforming operations are used in a multi-user multiple input multiple output (MU-MIMO) transmission mode.

3. The method of claim 1, wherein scanning with a grid of beams includes performing horizontal scanning within a progressive scanning period, wherein the grid of beams is configured to transmit the respective reference signals among a horizontal domain for a coverage area of the eNodeB.

4. The method of claim 3, wherein scanning with a grid of beams further includes performing vertical scanning within the progressive scanning period, wherein the grid of beams is configured to transmit the respective reference signals among an elevation domain for the coverage area of the eNodeB.

5. The method of claim 3, wherein performing horizontal scanning within a progressive scanning period includes conducting a horizontal scan for each beam group CSI sub-period in the plurality of beam groups; wherein sets of CSI reference signals provided in each beam group CSI sub-period are spaced among the plurality of beam groups with a periodicity of a plurality of subframes.

6. The method of claim 5, wherein the periodicity of the plurality of subframes is based on a period of: 5, 10, 20, 40, or 80 subframes.

7. The method of claim 3, wherein performing horizontal scanning within a progressive scanning period includes conducting a high-density horizontal scan, wherein multiple CSI reference signals are included within one frame.

8. The method of claim 7, wherein a pattern of the high-density horizontal scan repeats in direct or reverse order every m·n subframes, where m=a number of the plurality of beam groups and n=2, 4, 8, or 16.

9. The method of claim 1, wherein feedback parameters used with conducting the scanning are calculated by a k-th UE connected to the eNodeB, the k-th UE providing the feedback parameters on channel Hk for transmission to the eNodeB in a feedback procedure.

10. The method of claim 9, wherein the best beam index is identified in the feedback procedure and is progressively forwarded to the eNodeB; and wherein, at an end of a period for scanning with the grid of beams, the eNodeB is adapted to identify a Pre-coding Matrix Indicator PMI*k for the k-th UE, wherein

11. The method of claim 1, wherein a respective UE calculates the strength of a particular beam by measuring signal-to-interference-plus-noise ratio (SINR) with a corresponding CSI reference signal; and wherein, at the beginning of a feedback procedure, a Modulation and Coding Scheme (MCS) index corresponding to a Channel Quality Indication CQI0=maxi=0, . . . , 7|Hkφ0,i|2 is provided to the eNodeB.

12. An evolved NodeB (eNodeB), comprising circuitry arranged to perform progressive channel state information (CSI) acquisition with operations to: perform a vertical CSI acquisition for an elevation domain;perform a horizontal CSI acquisition by scanning a horizontal domain with a grid of beams, wherein the scanning is customized to the elevation domain;collect progressive CSI feedback from the horizontal CSI acquisition, wherein the eNodeB determines a set of Pre-coding Matrix Indicators PMI*={PMI*1, PMI*2, . . . , PMI*K}, wherein the set of PMI* is a set indicating best beam indices for active UEs connected to the eNodeB;wherein the PMI* is used to adapt different beamforming schemes for subsequent transmissions from the eNodeB to the active UEs.

13. The eNodeB of claim 12, the circuitry arranged to perform a determination of a PMI value for a particular User Equipment (UE), in the set of PMI* by: determining a highest CSI in the grid of beams in response to scanning with the grid of beams;receiving, from the particular UE, an indication of a best beam index in response to a strongest beam received at the UE from the grid of beams; andselecting the highest CSI in the grid of beams for the particular UE based on the best beam index.

14. The eNodeB of claim 12, wherein the beamforming schemes for subsequent transmissions are used in a multi-user multiple input multiple output (MU-MIMO) transmission mode.

15. The eNodeB of claim 12, wherein the operations to perform the horizontal CSI acquisition by scanning include conducting a horizontal scan for each beam group CSI sub-period in a plurality of beam groups; and wherein sets of CSI reference signals provided in each beam group CSI sub-period are spaced among the plurality of beam groups with a periodicity of a plurality of subframes.

16. The eNodeB of claim 15, wherein the periodicity of the plurality of subframes is directed to a period of: 5, 10, 20, 40, or 80 subframes.

17. The eNodeB of claim 12, wherein the operations to perform the horizontal CSI acquisition by scanning include conducting a high-density horizontal scan, wherein multiple CSI reference signals are included within one frame, wherein a pattern of the high-density horizontal scan repeats in direct or reverse order every m·n subframes, where m=a number of beam groups and n=2, 4, 8, or 16.

18. The eNodeB of claim 12, wherein feedback parameters for scanning are calculated by a k-th UE connected to the eNodeB with channel Hk for transmission to the eNodeB in a feedback procedure; wherein the best beam index is identified in the feedback procedure and is progressively forwarded to the eNodeB; andwherein, at an end of a period for scanning with the grid of beams, the eNodeB is adapted to identify PMI*k for the k-th UE, wherein

19. A user equipment (UE), comprising: multiple antennas arranged to receive transmissions from an evolved NodeB (eNodeB), the eNodeB operating in accordance with a standard from a 3GPP Long Term Evolution (LTE) standards family;multiple transceivers operably coupled to the multiple antennas and arranged to receive and transmit wireless communications from the eNodeB, the wireless communications including transmissions received from the eNodeB from a channel state information (CSI) scan performed on a vertical domain and on a horizontal domain; andprocessing circuitry arranged to process the transmissions received from the eNodeB from the CSI scan and provide progressive CSI feedback in response to the transmissions received from the eNodeB from the CSI scan, wherein an indication of a best beam index is progressively provided in the CSI feedback in response to a determination of a strongest beam received at the UE from the CSI scan.

20. The UE of claim 19, wherein the processing circuitry is further arranged to transmit, to the eNodeB, the indication of the best beam index in response to the strongest beam received at the UE from a grid of beams of the CSI scan; wherein the eNodeB operates to determine a highest CSI in the grid of beams for the UE based on the best beam index.

21. The UE of claim 19, wherein the CSI scan performed on the horizontal domain includes a horizontal scan for each beam group CSI sub-period in a plurality of beam groups; and wherein sets of CSI reference signals provided in each beam group CSI sub-period are spaced among the plurality of beam groups with a periodicity of a plurality of subframes, wherein the periodicity of a plurality of subframes is directed to a period of: 5, 10, 20, 40, or 80 subframes.

22. The UE of claim 19, wherein the CSI scan performed on the horizontal domain includes a high-density horizontal scan, wherein multiple CSI reference signals are inserted within one frame.

23. The UE of claim 21, wherein a pattern of the high-density horizontal scan repeats in direct or reverse order every m·n subframes, where m=a number of beam groups and n=2, 4, 8, or 16.

24. The UE of claim 19, wherein feedback parameters for the scanning are calculated by the UE for transmission to the eNodeB in a feedback procedure, wherein the best beam index is identified in the feedback procedure and is progressively forwarded to the eNodeB.