Patent Application
20160373170
Field of the Disclosure
The present disclosure relates generally to antennas and multiple-input, multiple-output (MIMO) antennas systems with diversity reception, and more particularly to mobile devices employing such MIMO antenna systems.
Background
Mobile devices may incorporate multiple antennas, or an antenna array, for diversity reception and for implementing spatial multiplexing. Spatial multiplexing involves splitting a high data rate signal into two or more separate data streams that are intended to arrive at a receiver antenna array with different spatial signatures such that the two or more separate data streams can be reassembled to construct the high data rate signal. At least two separate mobile device antennas, or two antenna elements of an antenna array, each receive one of the separate data streams. Therefore, spatial multiplexing may be considered a form of antenna diversity reception.
The goal of antenna diversity reception is to take advantage of decorrelation between the diversity antennas. The decorrelation may be achieved by physical placement, polarization or by using differing antenna beam patterns. Mobile device diversity and MIMO (multiple-input, multiple-output) antenna systems have been developed based on static figure-of-merit (“FoM”) requirements, total efficiency, gain imbalance and envelope correlation coefficient values (i.e. antenna correlation) that are fixed regardless of prevalent operating parameters or the environment in which the mobile device is operating.
Performance of the MIMO system may be negatively impacted by changes in the radiated channel conditions and the user's position and handgrip on the mobile device, because the hand position may impair radio frequency (RF) reception by the MIMO antennas. For this and other reasons, challenges exist for achieving good performance of diversity antenna systems in a mobile device.
Briefly, the disclosed embodiments provide an antenna augmentation peripheral (AAP) selection and management system and methods of operation for a mobile device. A mobile device may include an AAP aggregator in accordance with the embodiments that invokes AAP devices to form a group and to utilize antennas of the AAP devices to form a multi-antenna input system (i.e. receive system) for the mobile device. The AAP aggregator evaluates, among other things, AAP device modality and estimates at least one radio frequency (RF) performance metric to select AAP devices to maximize that performance metric during operation.
One aspect of the present disclosure is a method of operation for a mobile device. The method includes accessing antenna equipment of at least a first antenna augmentation peripheral (AAP) using a first set of radio channels in a first frequency band for communication between the mobile device and the first AAP and using a second set of radio channels in a second frequency band for communication with a wide area network (WAN) to form a multiple input antenna system. The mobile device receives a first performance metric for at least one channel of the second set of radio channels measured by the first AAP that is sent to the mobile device using the first set of radio channels. The mobile device determines a composite performance metric using the first performance metric and a second performance metric related to the second set of radio channels. The composite performance metric is reported to the WAN as the mobile device performance metric. The mobile device may then receive a downlink radio resource assignment from the WAN based on the composite performance metric.
The mobile device may obtain the second performance metric for a second channel from a second AAP. The mobile device can allocate the downlink radio resource assignment to one of the first AAP or the second AAP based on the first performance metric or the second performance metric. The mobile device may also estimate a coding rate gain for the second set of radio channels in communication with the WAN using historical data or empirical data for the first AAP and the second AAP, and may use the coding rate gain estimation to select the first AAP and the second AAP from a group of AAPs.
The mobile device may obtain additional radio resource allocations by implementing a short burst data traffic session as a speed test of the WAN for high bandwidth operation, and estimating a coding rate gain for the multiple input antenna system comprising the first AAP and the second AAP. The composite performance metric using the estimated coding rate gain can be determined and reported to the WAN to obtain additional downlink radio resource assignments. In one approach, the mobile device may aggregate application data to simulate a high bandwidth, full buffer condition to implement the short burst data traffic session.
The first set of radio channels in the first frequency band for communication between the mobile device and the first AAP may utilize an unlicensed frequency band while the second set of radio channels in the second frequency band for communication with a WAN may use a licensed frequency band.
Another aspect of the present disclosure is a method of operation for a mobile device where the mobile device may obtain modality data from a group of AAPs by communicating with each of the AAPs using a wireless interface and determine the available AAPs based on the modality data. The mobile device may assign radio channels of a first set of radio channels in a first frequency band to each available AAP for communicating with the mobile device, and may assign radio channels of a second set of radio channels in a second frequency band to each available AAP for communicating with a WAN. The mobile device uses the AAPs to form a multiple input antenna system for the mobile device.
The mobile device measures signal-to-noise-plus-interference ratio (SINR) for available radio channels in the second set of radio channels and creates an ordered list of available radio channels based on the SINR. The ordered list of available radio channels is then mapped to the AAPs.
The modality data may be obtained as a packet having a plurality of bit fields, with each bit field containing AAP operating mode information. The mobile device may assign a numerical value to each bit field such that each packet received from each AAP has an associated numerical value related to availability. The mobile device may then create a list of AAP's using the numerical value and may select available AAPs of the group by selecting each AAP based on its associated numerical value.
Another aspect of the present disclosure is a mobile device that includes antenna equipment with one or more antennas; radio transceiver equipment, operatively coupled to the antenna equipment, non-volatile, non-transitory memory comprising a composite channel quality indicator (CQI) table for determining a composite CQI for a first channel and a second channel in a multiple-input antenna system; and a processor operatively coupled to the memory and to the radio transceiver equipment. The mobile device is operative to perform all of the methods of operation and processes described in the present disclosure.
The transceiver equipment is operative to communicate with one or more AAPs using a first set of radio channels in a first frequency band to form a multiple input antenna system, and operative to communicate with a WAN using a second set of radio channels in a second frequency band. The processor is operative to access antenna equipment of at least a first AAP, receive a first CQI value for at least one channel of the second set of radio channels measured by the first AAP, determine a composite CQI using the first CQI value and a second CQI value related to the second set of radio channels, report the composite CQI to the WAN as the mobile device CQI, and obtain a downlink radio resource assignment from the WAN based on the composite CQI.
Turning now to the drawings,
Each of the AAPs, whether trusted devices 109 or public devise 111, are capable of communication with a wide area network (WAN) infrastructure 103 over a wireless interface 107 such as a Long Term Evolution, 4th Generation (4G LTE) wireless interface. The WAN infrastructure 103 includes base station equipment (such as eNode B), Evolved Packet Core Network, and any other equipment, including any legacy equipment, necessary for implementing the WAN. The WAN infrastructure 103 also includes an authentication, authorization, and accounting (AAA) entity, namely AAA server 105, which authenticates any device interacting and communicating with the WAN infrastructure 103.
The mobile device 101 also communicates with the WAN infrastructure 103 using the wireless interface 107, and also communicates with an APP aggregation entity 113. In the various embodiments, the AAP aggregation entity 113 may be a component of the mobile device 101, may be included in the WAN infrastructure 103, or may be distributed between the mobile device 101 and the WAN infrastructure 103. The AAP aggregation entity 113 determines and lists available AAPs that the mobile device 101 may access and utilize to enhance receive antenna diversity and bandwidth. The AAP aggregation entity 113 obtains modality data from the AAPs and assigns AAP groups that may best provide antenna augmentation for the mobile device 101 based on location and historical data collected for certain AAPs and AAP groups over time. A database of such information may be contained in the mobile device 101, in the WAN infrastructure 103 or distributed between the mobile device 101 and the WAN infrastructure, just as can be done with the AAP aggregation entity 113.
An AAP group 200 of selected AAPs is designated by the AAP aggregation entity 201 and identified to the mobile device 101 over the wireless interface 107. The members of the AAP group 200 are authenticated by the AAA server 105 and connection information is provided to the mobile device 101 by the AAP aggregation entity 201. The mobile device 101 uses the connection information to connect and communicate with each AAP of the AAP group of selected AAPs to achieve a MIMO receive antenna configuration, more particularly a multiple input antenna receive system. In accordance with the example embodiment of
Examples of AAP devices may include, but are not limited to, laptop computers, personal digital assistants (PDA), a tablet or e-book readers, mobile telephones or smartphones, smartwatches, mobile hotspots or other devices that include the appropriate antenna system and transceivers to at least receive and up/down convert a radio signal and transmit the converted signal over an unlicensed band. Additionally, some AAP devices may, from time-to-time, establish a network connection with an AAP aggregation entity such as AAP aggregation entity 201 or AAP aggregation entity 113 shown in
Modality data includes information regarding the mode of use of the AAP (or mobile device) such as the AAP's movement and speed, orientation in space, how the AAP is being held with respect to user grip on the AAP device, applications running on the AAP device, etc., that can be used to determine how the AAP is being used at a given point in time. For example, when the AAP is a wearable device such as a smartwatch, the modality data may indicate that the AAP user is running and using a heart rate monitor feature of the smartwatch. In another example where the AAP is a mobile device, the modality data may indicate that the mobile device user is engaged in a phone call. The modality data may be used to determine, among other things, whether a potential AAP is engaged in a connection with the WAN infrastructure 103 such that it is not a candidate for use as an AAP. Further details of an example AAP 300 are provided in
In the present disclosure the term AAP includes both “passive” AAP devices and “active” AAP devices. A passive AAP is one that includes an antenna system and radio frequency (RF) up/down conversion capability but no signal decoding capability. In other words, a passive AAP includes an RF subsystem but does not include a baseband component. An active AAP is one that includes the baseband component in addition to the antenna system such that it can decode signals. For example, the mobile device 101 shown in
Turning to
The transceivers 303 are operative to receive one or more data streams over the wireless interface 107 and to report CQI (channel quality indicator) and CSI (channel state information) to a mobile device using unlicensed bands 203 of the wireless interface 107 (such as 4G LTE unlicensed bands). The controller 320 is also operative to create a CQI table 327 that associates modalities from the modality table 329 with CQI values when the AAP 300 operates to receive data streams. The CQI tables 327 may be reported to an AAP aggregation entity of a mobile device or to a network based AAP aggregation entity. The controller 320 is operative to receive commands from a mobile device as described below, to tune to desired channels in order to receive data streams for which CQI is measured and reported.
In
The sensor hub 417 is operatively coupled to various sensors 418 which may include thermal sensors, proximity sensors, accelerometers, gyroscopic sensors, light sensors, etc. The sensor hub 417 is also operatively coupled to a set of touch sensors 419 which are positioned about the housing of the mobile device 400 and which are operative to sense the user's hand and fingers when placed upon the housing, and to send data to the sensor hub 417. The touch sensors 419 may be optical sensors, capacitive sensors, or combinations of both. The sensor hub 417 is a low power processor that offloads the processor 410 from some tasks such as obtaining data from the sensors 418 and from touch sensors 419. For example, the sensor hub 417 may provide functions while the processor 410 is placed in a sleep mode in order to conserve mobile device 400 battery power. The sensor hub 417 is operative to receive data from the various sensors and to convey the data to the processor 410 over the internal communication bus 405. The sensor data is therefore related to the mobile device 400 modality at any given point in time.
The mobile device 400 antennas 407 may include various MIMO diversity antennas. The antennas 407 are operatively coupled to the transceivers 402 by RF coupling 411. In some embodiments, the antennas 407 may also be operatively coupled to antenna selection and tuning logic 403 by RF coupling 411, and to the transceivers 402. Each antenna or antenna array may be evaluated by a figure-of-merit (FoM) or by signal quality metrics.
The processor 410 is operative to execute instructions (also referred to herein as “executable instructions,” “executable code” or “code”) stored in memory 415, including operating system executable code 431 to run at least one operating system 430, wireless protocol stack code 451 to run one or more wireless protocol stacks 450, and application (or “user space”) executable code 441 to run one or more applications 440.
In some embodiments, the processor 410 is also operative to execute condition prediction code 421 to implement condition prediction logic 420, and to execute AAP aggregator code 461 to implement AAP aggregator 460. The condition prediction logic 420 may interact and communicate with the operating system 430 by one or more APIs of a suite of APIs 423 (application programming interfaces) or by other appropriate operative coupling. The condition prediction logic 420 is operative to communicate with the sensor hub 417 to obtain data from the touch sensors 419, the sensors 418 or combinations thereof. This modality data may include information about the position of the mobile device 400, such as whether the mobile device 400 is stationary, in a docking station, placed flatly on a table surface, etc. and other information related to the ambient environment surrounding the mobile device 400. The location detection logic 409 may also be accessed by the condition prediction logic 420 to obtain location information for the mobile device 400. The condition prediction logic 420 may collect and aggregate this data into a user profile 425 stored in memory 415.
The data contained in the user profile 425 is time and date stamped and geotagged using location data from the location detection logic 409. The operation of the condition prediction logic 420 may also be present in an AAP such as AAP 300 described in
The AAP aggregator 460 communicates with the operating system 430 using an API 424 and with the wireless protocol stacks 450 using an API 462, and is operative to obtain SNR and CQI measurements form the transceiver/s 402 for at least two MIMO streams in a rank 2 transmission. The AAP aggregator 460 may access a CQI and coding rate tables 427 stored in memory 415, and also a modality table 429 in some embodiments. The AAP aggregator 460 is also operative to request modality data from potential AAPs and to assign rankings to the AAPs based on received modality data. The AAP aggregator 460 may control the mobile device 400 to establish connections with AAPs and to assign communication channels over licensed and unlicensed bands. Therefore the processor 410 may also communicate with controller 320 of the example AAP 300 and may command the AAP 300 to tune to any of a first set of channels of a first frequency band (such as an unlicensed band) to communicate with the mobile device 400 and to tune to any of a second set of channels of a second frequency band (such as a licensed band) to communicate with a WAN. These commands may initially be sent using a wireless interface such as IEEE 802.11x (WiFi®), Bluetooth®, etc., and may subsequently be sent over the unlicensed band after the AAP accordingly tunes to its assigned channels in response to the mobile device command.
In some embodiments, the AAP aggregator 460 may interact with the condition prediction logic 420 to obtain predicted modality information and may determine when to invoke AAP connections based on these predictions by accessing the modality table 429 and CQI and coding rate tables 427 and obtaining an expected coding rate (or expected CQI values) for previously used AAPs and the predicted modality. Further details of these operations are described below.
It is to be understood that any of the above described software components (i.e. executable instructions or executable code) in the example mobile device 400 or any of the other above described components of example mobile device 400 may be implemented as software or firmware (or a combination of software and firmware) executing on one or more processors, or using ASICs (application-specific-integrated-circuits), DSPs (digital signal processors), hardwired circuitry (logic circuitry), state machines, FPGAs (field programmable gate arrays) or combinations thereof. Therefore the mobile devices illustrated in the drawing figures described herein provide examples of a mobile device and are not to be construed as a limitation on the various other possible mobile device implementations that may be used in accordance with the various embodiments.
More particularly, condition prediction logic and/or the AAP aggregator may be a single component or may be implemented as any combination of DSPs, ASICs, FPGAs, CPUs running executable instructions, hardwired circuitry, state machines, etc., without limitation. Therefore, as one example, the condition prediction logic may be implemented using an ASIC or an FPGA. In another example, the AAP aggregator may be a combination of software or firmware executed by a processor that makes the decision regarding when to switch to establish connections with and/or use a particular AAP etc. These example embodiments and other embodiments are contemplated by the present disclosure.
Turning to
In any of the above possible embodiments, the AAP-RS bitmap 500 includes a user interaction state field 501, a device context field 503 and a user context field 505.
The user context field 505 may include bit fields for static state, walking state, activity state, and driving state. Alternatively, these states can be covered by a two bit field where “00” indicates a driving state, “01” indicates an activity state, “10” indicates a walking state, and “11” indicates a static state. The bitmap 500 may also include parity bits and/or a cyclic redundancy check (CRC) field in some embodiments. The bitmap 500 may also be extended to include location information in some embodiments. For example a field for GPS coordinates may be included. The AAP-RS bitmap 500 bit fields may be viewed as a numerical value based on the resulting binary number or hexadecimal number resulting from the populated bit fields. This individual bit fields numerical values, the entire AAP-RS bitmap 500 numerical value or both, may be considered a “score” related to the “availability” of the AAP. For example, a battery charge under twenty-five percent would result in a lower score for the related bit field than an AAP battery charge above twenty-five percent. In this way, an overall score may be determined such that AAPs may be placed in an ordered list based on the score, which is related to the AAP-RS bitmap 500 numerical value and overall AAP modality. The “availability” of an AAP as used herein may therefore be related to the overall score with some AAPs having higher availability scores than others. The AAPs with the highest availability scores would therefore be selected first and some AAPs would be rejected as unavailable (for example AAPs already engaged in a WAN connection or serving another mobile device as an AAP, etc.).
Returning briefly to
One process in a mobile device related to forming an AAP group is illustrated by the flowchart of
The AAP aggregation entity may collect the AAP-RS information as shown in
In embodiments in which the aggregation entity resides on the mobile device, such as in
For public devices 111 the AAP aggregator 460 may collect this historical data whenever it uses one of the public devices 111 as an AAP where such public devices 111 are at a fixed location or on public transportation. Such public device AAPs may operate similar to wireless local area (WLAN) access points (or may be WLAN access points) in that they may have a detectable BSSID and use associated connection security protocols. In the case of using other mobile devices as AAPs (i.e. a bandwidth sharing type of arrangement), a network based AAP aggregation entity 201 can obtain real time SINR information and AAP-RS bitmaps 500 from the mobile devices to determine appropriate AAP groups and send that information to the mobile device 101. In other words, the AAP aggregation function may be done locally in some instances, via a network entity, or by combinations of both. The public devices 111 that are other mobile devices require authentication by the AAA server 105 and the connectivity information for these devices is sent to the mobile device 101 by the network based AAP aggregation entity 201. For trusted devices 109, these devices inherit the trust relationship with the mobile device 101 as the master device as is accomplished when forming tethered connectivity such as through 802.11x or Bluetooth® connection establishment.
When the AAP aggregator 460 forms an AAP group for MIMO purposes, the selection of the unlicensed bands takes into consideration the avoidance of co-channel and adjacent channel interference and assigns the channels to the AAPs accordingly. In some embodiments, a frequency hopping scheme can also be employed on the AAP to mobile device channels to further improve performance and reduce channel interference as well as RF fading aspects.
As known by those of ordinary skill, standards map CQI to coding rate. The 3GPP TS 36.213 V12.1.0 (2014-03); 3rd Generation Partnership Project; Technical Specification Group Radio Access Network; Evolved Universal Terrestrial Radio Access (E-UTRA); Physical layer procedures (Release 12) (2014), which is hereby incorporated by reference herein, defines the “Channel Quality Indicator (CQI)” and provides a table mapping CQI index to coding rate for a SISO (Single-Input Single Output) system. In accordance with the embodiments, a mapping adapted to a rank 2 MIMO transmission is used that represents a composite CQI when AAPs are employed for MIMO reception. The coding rate translates to throughput in bits-per-symbol (bps).
For the present embodiments the inventors have developed a CQI equivalence table for a composite CQI for a rank 2 system with channel 0 and channel 1 (CQI-Ch0, CQI-Ch0) where a CQI from a first AAP observation and a second AAP observation may be used to determine a composite CQI from the lookup table. This table is stored in the CQI and coding rate tables 427. Another form of the table maps channel 0 and channel 1 CQI values to a composite coding rate in bits-per-second-per-Hertz (b/s/Hz) and this table can also be stored in the CQI and coding rate tables 427. Either or both of these tables may be accessed by the AAP aggregator 460. An example of the CQI composite table 900 is provided as
Further details of operation of a mobile device, such as mobile device 400, using the CQI and coding rate tables 427 are provided in the flowchart of
The coding rate gain estimation in operation block 805 may include an open loop component and a closed loop component. For example the open loop gain can be considered to be the summation of the selected (or candidate) AAP gains such that an Open Loop CSI Gain-Sigma*Fn(AAP)*=CodeRateGainOL. The expected coding rate gains may then be mapped to a low value or to empirically derived data (i.e. past historical measurements for the AAPs as described above). This approach may be used for cold-starts, that is when the mobile device forms a new AAP group. Based on antenna efficiencies, a baseline is determined. For example, the past measurements stored in memory may include data for an automobile AAP versus a smartwatch AAP. The closed loop gain can then may use of this historical data based on the achieved coding rate using the particular AAP device (or for a particular AAP group). Referring to the closed loop AAP coding rate gain as CodeRateGainCL; then CSI Gain=MapCQI(CodeRateGainCL|CodeRateGainOL). For example, in the open loop the coding rate gains can be started from a base-value and can be incremented at the rate of 0.25 bits/sym/Hz (bits-per-symbol-per-Hertz).
In operation block 807, once CSI-gain has been computed, it is normalized across the channels (Channel0 and Channel1). In one example, if the CQI (coding rate) for Ch0 and Ch1 are {7(1.48), 3(0.38)}, then the CSI-gain is normalized across CQI values. In this case this could be, for example, {7.5(1.73), 34(0.63)}. In operation block 809, this “enhanced” CSI/CQI feedback is reported to the WAN infrastructure. In some embodiments, the CQI on the channels that is reported to the WAN infrastructure in operation block 809 (i.e. the Ch0 to Ch1 CQI ratio) can be adjusted by the AAP aggregator between AAPs so as to influence resource grants in view of the coding rate improvement. Accordingly, the AAP aggregator can influence resources utilized by specific preferred AAP devices. This can be advantageous for a number of reasons such as, but not limited to, AAP modality changes for example a reduction in power state below 25 percent or some other modality change. Accordingly in operation block 811, the mobile device receives the PDCCH (Physical Downlink Control Channel) with the DL assignment from the WAN infrastructure having the MCS (modulation and coding scheme) for channel 0 and channel 1. More generally, downlink radio resources are assigned to the mobile device. The term “radio resources” refers to time-frequency resources such as in an orthogonal-frequency-division-multiplexed (OFDM) system such as in the LTE downlink.
In operation block 813, the AAP aggregator checks the coding rate for the downlink assignments and checks whether the channel 0 and channel 1 CQI meets the target coding rates. In decision block 815, if the target coding rates are met, then in operation block 817 the mobile device can send enhanced CSI/CQI feedback that includes the CSI gain plus a CSI gain increment. The “CSI_Gain_Increment” is a variable value which is set to 0.25 bits/sym/Hz in one example embodiment. If in decision block 815 the target coding rates are not met in decision block 815, then the process returns to operation block 809.
In another embodiment, the CSI gain may be estimated is by using the CQI equivalence or coding equivalence which is mapped out from the component CQI for channel 0 and channel 1. More particularly, the objective is to have a single CSI_gain value and distribute it across the CQI for both codewords. The closed loop operation tracks successful decodes for the CSI-gain reported to the WAN and maintains the history for the AAP group and CSI-gains from empirical data stored in memory. The initial guidance from open-loop estimate is conservative and is slowly ramped. However better DL assignments and successful decodes increase the confidence of the CSI-gain computation.
One approach to conducting the speed test is to assemble other data such as email downloads, application synchronizations to servers or other low rate data into packets appropriate to simulate high demand data. In other words, the lower rate data is used to create the appearance of a data flood or full data buffer scenario at the mobile device from the perspective of the WAN so as to artificially induce a maximum downlink allocation from the WAN. In operation block 1107, the mobile device estimates the coding rate gain for an AAP system using the speed test data, i.e. the CQI values observed during the test. In operation block 1111, after estimation of the coding rate gain (and CSI-gain determination), it is normalized across the channels (Channel0 and Channel1) as described in the process shown in
While various embodiments have been illustrated and described, it is to be understood that the invention is not so limited. Numerous modifications, changes, variations, substitutions and equivalents will occur to those skilled in the art without departing from the scope of the present invention as defined by the appended claims.
