HK1123142A - Robust rank prediction for a mimo system - Google Patents

Abstract

Techniques for performing rank prediction in a MIMO system are described. Performance metrics for a plurality of ranks are initially determined. Each rank is indicative of a different number of data streams to send simultaneously via a MIMO channel. The performance metrics may relate to the capacity or signal quality of the MIMO channel or the throughput of data transmission sent via the MIMO channel. Adjustments are applied to the performance metrics for the ranks to obtain adjusted performance metrics. The adjustments account for system losses such as losses due to an error correction code used for data transmission, channel estimation errors at a receiver, variation in interference observed by the receiver, variability in transmit power due to power control, and/or other factors. A rank is selected for use based on the adjusted performance metrics for the ranks.

Description

Robust rank prediction for MIMO systems

Claiming priority according to 35U.S.C § 119

U.S. patent provisional application No. 60/691,723 entitled "road RANK preliminary INTERFERENCE, POWER CONTROL, CHANNEL information, packet records AND analysis CONTROL," filed earlier than 16/6/2005, assigned to the assignee hereof AND incorporated herein by reference.

Technical Field

The present disclosure relates generally to communication, and more specifically to techniques for transmitting data in a multiple-input multiple-output (MIMO) system.

Background

In a wireless communication system, a transmitter (e.g., a base station or a terminal) may transmit data using multiple (T) transmit antennas to a receiver equipped with multiple (R) receive antennas. The multiple transmit and receive antennas form a MIMO channel, which may be used to increase throughput and/or improve reliability. For example, a transmitter may transmit T data streams simultaneously from the T transmit antennas to improve throughput. Alternatively, the transmitter may repeatedly transmit a single data stream from all T transmit antennas to improve reception by the receiver.

Causing interference from each transmit antenna transmission to transmissions from other transmit antennas in some cases, improved performance may be achieved by simultaneously transmitting less than T data streams from the T transmit antennas-for example, a subset of the T transmit antennas may be selected and the transmit antennas not used for transmission sent data streams from each selected transmit antenna do not cause interference to the transmit antennas used for transmission-thus, improved performance of the data streams sent on the selected transmit antennas may be achieved.

Rank prediction refers to determining the rank of a MIMO channel or equivalently the number of data streams that may be simultaneously transmitted via the MIMO channel if too many data streams are sent, then excessive interference may be observed for each of the data streams and overall performance may suffer, conversely, if too few data streams are sent, the capacity of the MIMO channel may not be fully utilized.

There is therefore a need in the art for techniques to determine the rank of a MIMO channel.

Disclosure of Invention

Techniques for implementing rank prediction in a MIMO system in one embodiment, rank prediction is achieved by evaluating performance of different possible ranks of a MIMO channel and selecting a rank with best or near-best performance-in one embodiment, the rank prediction accounts for system losses, which may include any type of loss that may be observed due to data transmission

In one embodiment of rank prediction, initially determining performance metrics for a plurality of ranks each indicating a different number of data streams to be sent simultaneously via a MIMO channel the performance metrics may relate to capacity of the MIMO channel, throughput of data transmissions sent via the MIMO channel, signal quality of the MIMO channel, and so on, which would adjust the performance metrics applied to the plurality of ranks to obtain adjusted performance metrics for the ranks-the adjustment accounting for system losses, e.g., due to error correction codes used for data transmissions, channel estimation errors at a receiver, interference variations observed by the receiver, variability in transmission power due to power control, and/or losses due to other factors-then selecting a rank based on the adjusted performance metrics for the plurality of ranks may select the rank having the best adjusted performance metric as another option, a lowest rank having an adjusted performance metric within a predetermined percentage of a best adjusted performance metric may be selected, at least one Channel Quality Indicator (CQI) for the selected rank based on the adjusted performance metric for the selected rank is determined, the selected rank and CQI may be quantized and sent to a transmitter.

Various aspects and embodiments of the disclosure are described in further detail below.

Drawings

The features and nature of the present invention will become more apparent from the detailed description set forth below when taken in conjunction with the drawings in which like reference characters identify correspondingly throughout.

Fig. 1 shows a transmitter station and a receiver station.

Fig. 2 shows a processing unit at the transmitter station.

Fig. 3 shows a rank predictor implementing capacity-based rank prediction.

Fig. 4 shows a rank predictor implementing a throughput-based rank prediction.

Fig. 5 shows a capacity adjustment unit in the rank predictor.

Fig. 6 shows a process for implementing rank prediction.

Fig. 7 shows an apparatus for implementing rank prediction.

Detailed Description

The word 'exemplary' is used herein to mean 'serving as an example, instance, or illustration'. Any embodiment or design described herein as 'exemplary' is not necessarily to be construed as preferred or advantageous over other embodiments or designs.

Fig. 1 shows a block diagram of one embodiment of two stations 110 and 150 in a wireless communication system 100. for downlink (or forward link) transmissions, station 110 may be part of or may contain some or all of its functionality: a base station, an access point, a node B, and/or some other network entity. Station 150 may be part of or may contain some or all of its functionality: for a terminal, mobile station, user equipment, subscriber unit, and/or some other apparatus for uplink (or reverse link) transmission, station 110 may be part of a terminal, mobile station, user equipment, etc., and station 150 may be part of a base station, access point, node B, etc., station 110 is a transmitter of data transmission and equipped with multiple (T) antenna stations 150 is a receiver of the data transmission and equipped with multiple (R) antennas-each transmit antenna and each receive antenna may be a physical antenna or an antenna array.

At transmitter station 110, a Transmit (TX) data processor 120 receives traffic data from a data source 112, processes (e.g., formats, encodes, interleaves, and symbol maps) the traffic data according to a packet format, and generates data symbols, as used herein, data symbols are symbols for data, pilot symbols are symbols for pilot, and symbols are typically complex values-the data symbols and pilot symbols may be modulation symbols from a modulation scheme such as PSK or QAM. A data packet format for which pilots are known a priori by both the transmitter and receiver may indicate a data rate or information bit rate, a coding scheme or code rate, a modulation scheme, a packet size, and/or other parameters the packet format may also be referred to as a rate, a transmission format, or some other term TX data processor 120 demultiplexes the data symbols into M streams, where 1 ≦ M ≦ T and the data symbol streams are sent simultaneously via the MIMO channel determined by the rank provided by controller/processor 140 and may also be referred to as data streams, spatial streams, output streams, or some other term.

TX spatial processor 130 multiplexes pilot symbols for the M data symbol streams, performs transmitter spatial processing on the multiplexed data and pilot symbols, and provides T output symbol streams to T transmitters (TMTR)132a through 132T. Each transmitter 132 processes (e.g., modulates, converts to analog form, filters, amplifies, and frequency upconverts) its output symbol stream and generates a modulated signal. T modulated signals from transmitters 132a through 132T are transmitted from antennas 134a through 134T, respectively.

At receiver station 150, R antennas 152a through 152R receive the T modulated signals, and each antenna 152 provides a received signal to a respective receiver (RCVR)154 each receiver 154 processes (e.g., filters, amplifies, downconverts, digitizes, and demodulates) its received signal to obtain received symbols each receiver 154 provides received symbols for traffic data to a Receive (RX) spatial processor 160 and provides received symbols for pilot to a channel processor 194 the channel processor 194 estimates the response of the MIMO channel from station 110 to station 150 based on the received symbols for pilot (and possibly the received symbols for traffic data) and provides channel estimates to RX spatial processor 160 which performs MIMO detection on the received symbols for traffic data with the channel estimates and provides data symbol estimates. An RX data processor 170 processes (e.g., deinterleaves and decodes) the data symbol estimates and provides decoded data to a data sink 172.

Receiver station 150 may evaluate the channel conditions and send feedback information to transmitter station 110 that may indicate, for example: rank to be used for transmission, Channel Quality Indicator (CQI), packet format to be used for transmission, Acknowledgement (ACK) and/or Negative Acknowledgement (NAK) of packets decoded by receiver station 150, other types of information, or any combination thereof, the feedback information is processed (e.g., encoded, interleaved, and symbol mapped) by TX signaling processor 180, spatially processed by TX spatial processor 182, and further processed by transmitters 154a through 154R to generate R modulated signals, which are transmitted via antennas 152a through 152R

At transmitter station 110, the R modulated signals are received by antennas 134a through 134t, processed by receivers 132a through 132t, spatially processed by an RX spatial processor 136, and further processed (e.g., deinterleaved and decoded) by an RX signaling processor 138 to recover data transmissions for feedback information based on which control/processor 140 controls receiver station 150.

Controllers/processors 140 and 190 control operating memories 142 and 192 at stations 110 and 150, respectively, to store data and program codes for stations 110 and 150, respectively.

The rank prediction techniques described herein may be used for any MIMO wireless communication system, e.g., a MIMO wireless communication system such as a Frequency Division Multiple Access (FDMA) system, a Code Division Multiple Access (CDMA) system, a Time Division Multiple Access (TDMA) system, a Space Division Multiple Access (SDMA) system, an Orthogonal FDMA (OFDMA) system, a single carrier FDMA (SC-FDMA) system, etc. OFDMA uses orthogonal frequency division multiple access (OFDM), and SC-FDMA uses single-carrier frequency division multiple access (SC-FDM). OFDMA and SC-FDMA partition the system bandwidth into multiple (K) orthogonal subcarriers, which are also referred to as tones, bins, and so on. Each subcarrier may be modulated with data. In general, modulation symbols are sent in the frequency domain over OFDM and in the time domain over SC-FDM.

The MIMO channel formed by the T antennas at transmitter station 110 and the R antennas at receiver station 150 may be formed by an RxT MIMO channel response matrix for each subcarrier kH(k) Is characterized by said matrixH(k) Can be expressed as:

wherein item h_i，j(k) (where i 1.. and R and j 1.. and T) are the coupling or composite gain of the transmit antenna j and receive antenna i for subcarrier k.

The MIMO channel may be decomposed into S spatial channels, where S ≦ min { T, R } the spatial channels may also be referred to as spatial layers, independent channels, etc. the MIMO channel response matrixH(k) May be diagonalized to obtain S eigenmodes of the MIMO channel, which may be considered orthogonal spatial channels by performing eigenbeamforming at the transmitter, may send S data symbol streams on the S eigenmodes, may also be transmitted by some other spatial processing or no spatial processing at the transmitterAnd transmitting S data symbol streams on the S spatial channels.

The number of eigenmodes (or number of spatial channels) is referred to as the rank of the MIMO channel which may be considered full rank if S ═ min { T, R } and less than full rank if S < min { T, R } is typically determined by channel conditions-for example, the rank is typically higher in radio channels with sufficient scattering and typically lower in space-dependent and line-of-sight (LOS) channels.

Good performance (e.g., higher overall throughput) may be achieved by transmitting data such that the number of data symbol streams matches the rank of the MIMO channel in a low rank channel, reducing the number of data symbol streams may substantially reduce inter-stream interference and increase the received signal quality of the transmitted data symbol streams, which may allow the streams to be sent at a higher rate and thus, a higher overall throughput may be achieved by fewer data symbol streams, as opposed to a full rank channel where the maximum number of data symbol streams may be sent to fully utilize all spatial channels of the MIMO channel and maximize MIMO gain.

Rank prediction techniques described herein determine the number of data symbol streams to transmit so that good performance may be achieved

The rank prediction techniques may be used for various operating modes such as Single Codeword (SCW) mode, in which a single packet format is used for all data symbol streams, and Multiple Codeword (MCW) mode, in which operation at the transmitter and receiver may be simplified, in which different packet formats may be used for each data symbol stream, which may increase performance under certain channel conditions.

The rank prediction technique may also be used for various spatial processing schemes such as a direct mapping scheme in which one data symbol stream is transmitted from each transmit antenna without any spatial processing, a pseudo-random mapping scheme in which each data symbol stream is transmitted from all T antennas and all data symbol streams achieve similar received signal quality in a beamforming scheme, each data symbol stream is transmitted on a different eigenmode and may achieve the same or different received signal quality-generally, signal quality may be quantified by signal-to-noise ratio (SNR), signal-to-noise-and-interference ratio (SINR), energy-to-noise-per-symbol ratio (Es/No), etc. for clarity, SNR is used to represent signal quality in the following description

For clarity, the rank prediction techniques are described below for an OFDM-based system (e.g., an OFDMA system). additionally, the techniques are described for SCW mode in the context of a pseudo-random mapping scheme

Fig. 2 shows a block diagram of one embodiment of TX data processor 120, TX spatial processor 130, and transmitters 132a through 132t at transmitter station 110 in TX data processor 120, encoder 210 encodes traffic data according to a coding scheme and generates code bits. The coding scheme may include a Turbo code, a convolutional code, a Low Density Parity Check (LDPC) code, a Cyclic Redundancy Check (CRC) code, a block code, etc., or a combination thereof, the channel interleaver 212 interleaves (or reorders) the code bits based on an interleaving scheme and provides interleaved bits. A symbol mapper 214 maps the interleaved bits according to a modulation scheme and provides a data symbol demultiplexer (Demux)216 to demultiplex the data symbols into M streams, where M is the predicted/selected rank of the MIMO channel and is provided by controller/processor 140

In TX spatial processor 130, a multiplexer (Mux)220 receives M data symbol streams from TX data processor 120 and maps the data symbols and pilot symbols to the correct subcarrier spatial mapping unit 222 in each symbol period, multiplies the data and/or pilot symbols for each subcarrier k by TxM spatial mapping matrix from matrix selector 224P _M(k) And provides a matrix of output symbols for the subcarriersP _M(k) The sub-matrix selector, which may be a TxT Fourier matrix, a TxT orthogonal matrix, or some other matrix, may determine the rank M based on the rank from the controller/processor 140P _M(k) The dimension matrix selector 224 may also provide different spatial mapping matrices to different subcarriers the spatial mapping unit 222The T output symbol streams are provided to T transmitters 132a through 132T.

Each transmitter 132 includes an OFDM modulator (Mod)230 and a TX Radio Frequency (RF) unit 232 within each transmitter 132, OFDM modulator 230 receives the output symbol stream and generates an OFDM symbol in each symbol period, OFDM modulator 230 performs a K-point IFFT on the K output symbols for the K subcarriers and appends a cyclic prefix to generate an OFDM symbol for the symbol period, TX RF unit 232 processes the OFDM symbol and generates a modulated signal.

At receiver station 150, the received symbols from receivers 154a through 154r may be represented as:

r(k)＝H(k)·P _M(k)·s(k)+n(k)＝H _M(k)·s(k)+n(k)， Eq(2)

where s (k) is the Mx1 vector of data symbols for subcarrier k,

r (k) is an Rx1 vector of data symbols for subcarrier k,

H _M(k)＝H(k)·P _M(k) an RxM effective MIMO channel response matrix that is subcarrier k, an

n(k) Is the Rx1 noise vector for subcarrier k.

For simplicity, it is assumed that the noise has a zero mean vector and covariance matrixn(k)＝σ_n ²·IAdditive White Gaussian Noise (AWGN) of (1), where σ_n ²Is the variance of the noise and I is the identity matrix.

Receiver station 150 may recover the data symbols sent by transmitter station 110 using various MIMO detection techniques, including: (1) linear MIMO detection techniques such as Minimum Mean Square Error (MMSE), forced Zero (ZF), and Maximum Ratio Combining (MRC) techniques; and (2) non-linear MIMO detection techniques, such as Maximum Likelihood (ML) Decoding, List-Sphere Decoding (LSD-List Sphere Decoding), Decision Feedback Equalizer (DFE), and Successive Interference Cancellation (SIC) techniques receiver station 150 may derive the following spatial filter matrix for each subcarrier k based on the MMSE, ZF, or MRC techniques:

D _mmse(k)＝[diag{Q _M(k)}]^-1，and

in equations (3) and (5),D _mmse(k) andD _mrc(k) is an MxM diagonal matrix of scaled values used to obtain normalized estimates of the data symbols.

Receiver station 150 may perform MIMO detection as follows:

whereinM(k) Can be thatM _mmse(k)、M _zf(k) OrM _mrc(k) The MxR spatial filter matrix of (a),is an Mx1 vector with M data symbol estimates, anIs a vector of noise after the MIMO detection.

Receiver station 150 may obtain based on pilot symbols received from transmitter station 110H(k) OrH _M(k) Is estimated. The receiver station 150 may then be based onH(k) OrH _M(k) To obtainM(k)。M(k) Is dependent onRank M used for transmission.The data symbol estimation in (1) iss(k) Is estimated.

In one embodiment, rank prediction is achieved by evaluating the performance of different possible ranks of the MIMO channel and selecting the rank with the best or near-best performance. Performance may be quantified by various metrics such as channel capacity, throughput, signal quality (e.g., SNR), and so on. Channel capacity generally refers to the theoretical transmission capacity of a communication channel. The capacity of a MIMO channel depends on the number of spatial channels in the MIMO channel and the signal quality of each spatial channel. Throughput generally refers to the amount of data sent over a communication channel. The throughput depends on the channel capacity and on system parameters, such as the available packet formats. Channel capacity and throughput may be given in terms of spectral efficiency, which is typically given in units of information bits per second per hertz (bps/Hz). In the following description, the channel capacity is simply referred to as capacity.

In one embodiment, the rank predictor accounts for system losses. As used herein, system loss refers to any type of loss that data transmission may experience. System losses may include system implementation losses (e.g., due to coding schemes, packet formats, etc.), losses due to channel variability (e.g., variability in interference and transmit power), processing losses (e.g., channel estimation errors), and/or other types of losses.

Fig. 3 shows one embodiment of a rank predictor 300 that implements capacity-based rank prediction and accounts for system losses. The rank predictor 300 evaluates the performance of each possible rank using the capacity as a performance metric. For simplicity, the following description assumes that T ≦ R, and that up to T data symbol streams may be sent simultaneously from the T transmit antennas. The rank predictor 300 includes T processing sections 310a to 310T for T possible ranks of m-1 to T, respectively. Each processing section 310 determines an average capacity for different possible ranks available for data transmission.

In a processing section 310 for rank m (where m e { 1.. T }), a spatial mapping unit 312 receives a MIMO channel response matrix for each subcarrier kH(k) Will beH(k) Txm spatial mapping matrix multiplied by rank mP _m(k) And provides Rxm effective MIMO channel response matrix for subcarrier kH _m(k) In that respect Assuming that m data symbol streams of rank m are transmitted, unit 312 performs spatial mapping in the same manner as spatial mapping unit 222 at transmitter station 110.

SNR calculation unit 314 determines the SNR of m data symbol streams of rank m or (equivalently) m spatial channels in the case of the MMSE technique described above, based first onH _m(k) DeterminingQ _m(k) The SNR per data symbol stream for rank m may then be expressed as:

wherein q is_m，i(k) Is a subcarrier kQ _m(k) The ith diagonal element of (1), and

SNR_m，j(k) SNR of a data symbol stream i being a subcarrier k

Equation (8) gives the SNR in linear units. For other MIMO detection techniques, the SNR is calculated in a different manner.

Then, the average SNR for all m data symbol streams of rank m may be calculated as follows:

wherein the SNR_avg，m(k) Is the average SNR for all m data symbol streams for subcarrier k.

The capacity mapper 316 measures the average SNR for each subcarrier k_avg，m(k) Mapping to capacity and further accumulating the capacity of all K subcarriers may be implemented based on an unconstrained capacity function as follows.

Wherein C is_avg，mIs the average capacity per spatial channel for rank m. In equation (10), the capacity per subcarrier is given by: log (log)₂[1+SNR_avg，m(k)]Then, the capacities of all K subcarriers are accumulated to obtain an average capacity of rank m. The unconstrained capacity function assumes no loss due to coding or modulation.

The capacity mapping may also be implemented based on a constrained capacity function as follows:

where η < 1.0 is a penalty factor that may account for various factors such as modulation scheme, coding rate, packet size, etc., and may also determine capacity based on other capacity functions or look-up tables

As described below, the capacity adjustment unit 318 adjusts the average capacity C_avg，mProviding adjusted capacity C of rank m to account for various factor units 318_adj，m。

Rank selector 330 all T possible adjusted capacities C of ranks 1 to T_adj，1To C_adj，T. Rank selector 330 first determines the following total capacity C for each rank m_total，m：

C_total，m＝m·C_avg，m. Eq(12)

Rank selector 330 then selects one of the T possible ranks in one embodiment, rank selector 330 provides the rank with the largest total capacity as follows:

in another embodiment, the rank selector 330 selects the lowest rank with total capacity within a predetermined percentage of the maximum total capacity as follows:

M＝min{arg(C_total，m＞β·C_max)}， Eq(14)

wherein, C_maxIs the maximum total capacity of all T possible ranks and β ≦ 1.0 the lower rank is generally more robust against deleterious channel conditions and channel estimation errors so if the lower rank can achieve a total capacity close to the maximum total capacity, it may be selected for use

CQI Generator 332 receives the adjusted capacity C of all T possible ranks and the selected rank M_adj，1To C_adj，T. In one embodiment, CQI generator 332 determines an adjusted capacity C for the selected rank M as follows_adj，MEffective SNR of (d):

wherein the SNR_eff，MGiving the CQI generator 332 a unit of decibel (dB) also allows the effective SNR to be determined by some other function or look-up table of SNR specific capacities.

In one embodiment, CQI generator 332 quantizes the effective SNR to a predetermined number of bits to obtain a CQI for the selected rank M. In another embodiment, CQI generator 332 maps the effective SNR to a packet format based on a rate look-up table of packet formats to desired SNRs for that packet format-this rate look-up table contains the desired SNRs for each packet format supported by the system-the minimum SNR required for reliably transmitting packets in an AWGN channel with a particular target Packet Error Rate (PER), e.g., 1% PER-the rate look-up table may be generated by computer simulation, empirical measurements, testing, and/or some other mechanism.

Fig. 4 shows one embodiment of a rank predictor 400 implementing throughput-based rank prediction and accounting for system losses the performance rank predictor 400 that evaluates each possible rank using throughput as the performance metric includes T processing sections 410a through 410T for T possible ranks of m 1 through T, respectively, each processing section 410 determining the throughput of a different possible rank that may be used for data transmission.

In processing section 410 for rank m (where m e { 1.. T }), units 412, 414, 416, and 418 operate SNR calculation unit 420 to receive an adjusted capacity C of rank m in the same manner as units 312, 314, 316, and 318, respectively, in fig. 3_adj，mAnd determines an effective SNR (e.g., as shown in equation (15)) rate lookup table 422 receives an effective SNR for rank m and provides a packet format with a maximum throughput and a desired SNR less than the effective SNR.

Rank selector 430 receives the throughputs TP of all T possible ranks₁To TP_TAnd determines a total throughput TP for each rank as follows_total，m：

TP_total，m＝m·TP_m. Eq(16)

Rank selector 430 then selects one of the T possible ranks-in one embodiment, rank selector 430 provides a rank with a maximum total throughput as follows:

in another embodiment, rank selector 430 selects the lowest rank with total throughput within a predetermined percentage of the maximum total throughput as follows:

M＝min{arg(TP_total，m＞β·TP_max)}， Eq(18)

wherein TP_maxMaximum total throughput of all T possible ranks

As shown in FIG. 4, CQI Generator 432 may receive the effective SNRs for all T possible ranks and provide the effective SNRs for the selected rank M as the CQIQIQI Generator 432 may also receive the packet formats for all T possible ranks and provide the packet format for the selected rank M as the CQI (not shown in FIG. 4)

Fig. 3 and 4 show that the two embodiments of rank prediction for performance metrics based on capacity and throughput, respectively, throughput can be considered as a quantized version of capacity, where the quantization determines the difference between capacity and throughput from the supported packet formats, typically decreasing with more supported packet formats.

Rank prediction may also be implemented based on other performance metrics in another embodiment, rank prediction may be implemented based on a signal quality (e.g., SNR) performance metric, e.g., an average SNR per subcarrier K for rank m may be determined and accumulated over the K subcarriers to obtain an average SNR for rank m, e.g., as shown in equation (9). Adjustments may then be applied to the average SNR for each rank m to obtain adjusted SNRs for that rank then the adjusted SNRs for the T possible ranks may be used to select one rank and determine CQI for the selected rank

Average capacity C in FIGS. 3 and 4_avg，mIs the capacity of each spatial channel in a MIMO system with rank m. The calculated average capacity C_avg，mSubject to various error sources such as, for example, channel estimation errors, the average capacity C_avg，mMay also be impractical for various reasons such as, for example, a limited set of packet formats supported by the system and available for data transmission, the capacity calculated at one time may be different from the capacity at another time when data is sent, for example, due to changes in channel conditions, interference, changes in transmission power, etc. furthermore, certain constraints may be imposed on the rank to select the average capacity C_avg，mMay be adjusted to account for the various factors.

Fig. 5 shows one embodiment of capacity adjustment unit 318x that may be used for each capacity adjustment unit 318 in fig. 3 and each capacity adjustment unit 418 in fig. 4 in capacity adjustment unit 318x, unit 510 adjusts the average capacity of rank m to account for coding losses different error correction codes may have different amounts of loss, which may be determined by the error correction capacity of the code, for example, a convolutional code may have a greater loss than a Turbo code for adjustment of coding losses, also referred to as gap-to-capacity compensation in one aspect, which may be calculated as follows:

C_gap，m＝log₂[1+SNR_avg，m/g]， Eq(20)

where g ≧ 1.0 is a factor accounting for coding loss different codes may be associated with different values of g.

Unit 512 adjusts the capacity of rank m to account for channel estimation errors in one aspect, this may be calculated as follows:

SNR_ch，m＝Channel_Backoff(SNR_gap，m，m，Channel model)，Eq(22)

C_ch，m＝log₂[1+SNR_ch，m]， Eq(23)

where Channel _ Backoff is a function that reduces the SNR of rank m to account for Channel estimation errors.

The amount of loss due to Channel estimation errors may depend on various factors, such as the rank of the MIMO Channel (e.g., more loss for higher rank), a Channel model (e.g., more loss for higher mobility), etc., which may be quantified by antenna configuration, mobility or Doppler and/or other factors, which may be determined based on computer simulation, empirical measurements, testing, and/or some other means, may be determined for different operating scenarios, such as different antenna configurations (e.g., 2 x 4, 4 x 2), different candidate ranks, different Doppler, etc., which may generally be stored in one or more look-up tables, may be defined for any number of operating scenarios and based on any number of input parameters and any type of input parameters, for example, one for each operating scenario.

In one aspect, unit 514 adjusts the capacity of rank m to account for interference variation, which may be calculated as follows:

SNR_int，m＝Interference_Backoff(SNR_ch，m，Interference variation)，Eq(25)

C_int，m＝log₂[1+SNR_int，m]， Eq(26)

where Interference _ Backoff is a function that reduces the SNR of rank m to account for the Interference variation observed by receiver station 150.

Receiver station 150 may measure interference over time and/or frequency and determine a change in interference based on the measurements. The amount of loss due to interference variations may be determined based on computer simulations, empirical measurements, testing, and/or some other means. The interface _ Backoff function may be stored in a look-up table.

Unit 516 may apply other adjustments to the capacity of rank m. In one embodiment, unit 516 may apply adjustments to account for: (1) a change in transmission power over time caused by power control, and/or (2) an offset between the transmission power of the pilot or control channel and the transmission power of the traffic channel. For example, unit 516 may decrease or increase capacity depending on whether the transmission power is decreased or increased in the upcoming interval. In one embodiment, unit 516 may disqualify rank m if m > 1 and the SNR for rank m is below a predetermined SNR. A low SNR may indicate that station 110 or 150 is near the edge of coverage and is a candidate for handoff. Disqualifying for rank m may result in selection of a lower rank (e.g., rank 1), which may be more robust under low SNR conditions. In one embodiment, unit 516 may adjust the capacity of rank m to account for H-ARQ packet termination latency. With H-ARQ, a packet is sent in one transmission and, if necessary, one or more retransmissions until the receiver station 150 correctly decodes the packet. H-ARQ packet termination latency refers to the average number of transmissions/retransmissions of a packet. More latency may indicate inaccuracy of the rank prediction. Thus, more compensation may be applied for more latency. In one embodiment, unit 516 may apply a bias to select a lower rank if variability of ranks is observed. In general, unit 516 may apply adjustments for any number and type of factors that may affect data transmission performance.

Unit 518 limits the capacity of rank m to a range of minimum and maximum values. Said minimum value is called the reference and is denoted C_floorAnd may be set to the lowest throughput for all supported packet formats. Said maximum value is called the ceiling and is denoted C_ceilingAnd may be set to the maximum throughput for all supported packet formats. The capacity of each rank m may then be constrained within the base and ceiling. In one aspect, this can be expressed as follows:

wherein C is_misc，mIs the capacity of rank m from unit 516 is in equation (27), is unmodified if the capacity of rank m is within the range of the base and ceiling, and is set to the ceiling if greater than the ceiling, and is set to zero if less than the base criteria set to zero means rank m will not be selected for use

In general, adjustments may be applied for any number and any type of factors. Fig. 5 shows that the adjustments applied for certain example factors may also apply the adjustments utilized for fewer, different, and/or additional factors, for example, the adjustments to the supported packet formats in equation (27) may be omitted as another example, and only the adjustments to the pair of channel estimation errors and interference variations may be applied that provide a margin for rank prediction in order to select an appropriate rank for use according to various possible sources of errors in rank prediction.

For clarity, fig. 5 shows separate units for applying adjustments for each factor, except for unit 516, however, the units may be integrated into one or more functional units, such as software, hardware, or combinations thereof, again for clarity, the adjustments for each factor are described separately-in general, the adjustments may be applied independently for each factor, for a subset of the factors, or for all considerations-furthermore, the adjustments may be applied in other orders than that shown in fig. 5-the adjustments may be applied using any number of functions and/or look-up tables with any number of input parameters and any type of input parameters.

In the embodiments shown in fig. 3 to 5, the average capacity applied to the spatial channels of each rank m is adjusted. Rank selectors 330 and 430 then determine the total capacity or total throughput for each rank and selecting the rank with the best or near-best performance to apply an adjustment to the average capacity may result in a higher rank with a larger granularity adjustment may also be applied to the total capacity or total throughput rather than the average capacity or average throughput.

Receiver station 150 may quantize selected rank M to a predetermined amount of bits, which may be determined based on the highest rank supported by the system, e.g., if the system supports a 4 x 4 configuration as the highest dimensional configuration, the highest possible rank is 4, and selected rank M may be transmitted using 2 bits.

Receiver station 150 may also quantize the CQI to a predetermined number of bits, which may allow reporting the CQI with finer granularity by more bits determined with a desired accuracy of the CQI, which may be beneficial for packet format selection the number of bits of the CQI may be based on (e.g., proportional to) the number of packet formats supported by the system-more packet formats may generally mean that a more accurate CQI may be beneficial for selecting a suitable packet format-the CQI may be quantized to 3, 4, 5, 6, or some other number of bits

Receiver station 150 may determine and report the rank and CQI periodically and at a rate fast enough to achieve good performance for data transmission may determine and report the rank and CQI at the same rate, e.g., every 5, 10, or 20 milliseconds (ms) alternatively, may determine and report the rank and CQI at different rates-e.g., may determine and report the rank at a first rate, and the rank at which the cqimo channel is determined and reported at a second rate may change at a slower rate than the SNR of the spatial channel and thus may be reported at a slower rate than the CQI

As shown in fig. 1, the rank and CQI may be determined by receiver station 150 and sent back to transmitter station 110 may also be determined by transmitter station 110 using information from receiver station 150, e.g., in a Time Division Duplex (TDD) system, the downlink and uplink share the same frequency channel, and the channel response of one link may be assumed to be the inverse of the channel response of the other link-in which case transmitter station 110 may be able to estimate the MIMO channel response based on pilots sent by receiver station 150-transmitter station 110 may then determine the rank and packet format for data transmission based on its estimate of the MIMO channel response

For clarity, rank prediction techniques have been described for the SCW mode-the techniques may also be used to select a rank for an MCW mode-the rank prediction for the MCW mode may be implemented as above for the SCW mode-for each candidate rank m, adjusting the capacity per spatial channel or the total capacity of all spatial channels that may be applied to rank m. The CQI for each spatial channel in the selected rank M may be determined. If M is greater than 1, more than 1 CQI may be generated.

Fig. 6 shows that one embodiment of a process 600 for implementing rank prediction determines performance metrics for multiple ranks (block 612) the performance metrics for each rank indicating a different number of data symbol streams to be sent simultaneously via a MIMO channel or, equivalently, a number of spatial channels to be used for data transmission may relate to a capacity of the MIMO channel, a throughput of data transmissions sent via the MIMO channel, a signal quality of the MIMO channel, etc. may determine the performance metrics for each of the ranks.

Performance metrics applied to the multiple ranks may be adjusted (block 614) to obtain adjusted performance metrics for the ranks, the adjustment accounting for certain system loss parameters the loss may be due to one or more of error correction codes used for data transmission, channel estimation errors at the receiver, interference variation observed by the receiver, loss due to variability of transmission power for power control, and/or other factors-furthermore, other loss parameters may be utilized the adjustment may be applied to SNR (as described above), capacity, and/or other metrics, all of which may be relevant-for example, SNR may be converted to capacity via a capacity function or look-up table, and vice versa performance metrics for ranks having performance metrics below a predetermined threshold may be omitted from consideration-may be limited to a range of values, which may be determined by the supported packet format, the adjustment may be applied using a look-up table, calculation, and/or some other means.

Selecting a rank from the plurality of ranks based on the adjusted performance metrics to select a rank having a best adjusted performance metric for data transmission (block 616). Another option may be to select a lowest rank having an adjusted performance metric within a predetermined percentage of the best adjusted performance metric to determine at least one CQI for the selected rank based on the adjusted performance metric for the selected rank (block 618). for example, one CQI may be determined for the SCW mode, whereas for the MCW mode it may be determined that M CQIs may be quantized SNRs, packet formats, or some other type of information if the rank prediction is implemented at the receiver, the selected rank and the CQIs may be quantized and sent to the transmitter

Process 600 may be implemented by controller/processor 190 at receiver station 150 or some other processor implementing process 600 may also be implemented by controller/processor 140 at transmitter station 110 or some other processor implementing may implement the adjustment using a look-up table stored in memory 192 at receiver station 150 or memory 142 at transmitter station 110.

Fig. 7 shows an embodiment of an apparatus 700 for implementing rank prediction, the apparatus 700 comprising: means for determining performance metrics for a plurality of ranks (block 712); means for applying adjustments to performance metrics of the plurality of ranks to obtain adjusted performance metrics of the ranks (block 714); means for selecting a rank from the plurality of ranks for data transmission based on the adjusted performance metric (block 716); and means for determining at least one CQI for the selected rank based on the adjusted performance metric for the selected rank (block 718).

The rank prediction techniques explained herein may be implemented by various means. For example, the techniques may be implemented in hardware, firmware, software, or a combination thereof. For a hardware implementation, the processing units used to implement rank prediction may be implemented within one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), field programmable logic arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, electronic devices, other electronic devices designed to perform the functions described herein, or a combination thereof.

For a firmware and/or software implementation, the rank prediction techniques may be implemented with instructions (e.g., procedures, functions, and so on) to perform the functions described herein. The instructions (e.g., software or firmware) may be stored in a memory (e.g., memory 192 shown in fig. 1) and executed by a processor (e.g., processor 190). The memory may be implemented within the processor or external to the processor.

The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (26)

1. An apparatus, comprising:

at least one processor configured to

Determining performance metrics for a plurality of ranks, each rank indicating a different number of data streams to be simultaneously transmitted via a multiple-input multiple-output (MIMO) channel,

applying an adjustment to the performance metrics of the plurality of ranks to obtain an adjusted performance metric, the adjustment accounting for system losses, an

Selecting a rank from the plurality of ranks for data transmission based on the adjusted performance metric; and

a memory coupled to the at least one processor.

2. The apparatus of claim 1, wherein the performance metric relates to a capacity of the MIMO channel.

3. The apparatus of claim 1, wherein the performance metric relates to a throughput of data transmissions sent via the MIMO channel.

4. The apparatus of claim 1, wherein the performance metric relates to a signal quality of the MIMO channel.

5. The apparatus of claim 1, wherein the adjustment accounts for losses due to error correction codes used for data transmission.

6. The apparatus of claim 1, wherein the adjustment accounts for channel estimation errors at a receiver.

7. The apparatus of claim 1, wherein the adjustment accounts for interference variations observed by a receiver.

8. The apparatus of claim 1, wherein the adjustment accounts for transmit power variations used for data transmission.

9. The apparatus of claim 1, wherein the at least one processor is configured to

Ranks having performance metrics below a predetermined threshold are omitted.

10. The apparatus of claim 1, wherein the at least one processor is configured to

Limiting the performance metrics for the plurality of ranks to within a range of values.

11. The apparatus of claim 10, wherein the range of values is determined by a packet format available for data transmission.

12. The apparatus of claim 1, wherein the at least one processor is configured to

The rank with the best adjusted performance metric is selected.

13. The apparatus of claim 1, wherein the at least one processor is configured to

Determining a best adjusted performance metric of the adjusted performance metrics for the plurality of ranks, and

selecting a lowest rank having an adjusted performance metric within a predetermined percentage of the best adjusted performance metric.

14. The apparatus of claim 1, wherein the at least one processor is configured to

Representing said selected rank by a predetermined number of bits, an

Transmitting the selected rank to a transmitter.

15. The apparatus of claim 1, wherein the at least one processor is configured to

Determining at least one Channel Quality Indicator (CQI) based on the adjusted performance metric for the selected rank.

16. The apparatus of claim 1, wherein the at least one processor is configured to

Determining a signal-to-noise ratio (SNR) based on the adjusted performance metric for the selected rank, an

Quantizing the SNR to obtain a Channel Quality Indicator (CQI) for the selected rank.

17. The apparatus of claim 1, wherein the memory is configured to store at least one lookup table for adjustments of the performance metrics.

18. A method, comprising:

determining performance metrics for a plurality of ranks, each rank indicating a different number of data streams to be simultaneously transmitted via a multiple-input multiple-output (MIMO) channel;

applying an adjustment to the performance metrics of the plurality of ranks to obtain an adjusted performance metric, the adjustment accounting for system losses; and

selecting a rank from the plurality of ranks for data transmission based on the adjusted performance metric.

19. The method of claim 18, wherein determining performance metrics comprises determining performance metrics for one or more of capacity of the MIMO channel, throughput of data transmissions sent via the MIMO channel, or signal quality of the MIMO channel.

20. The method of claim 18, further comprising calculating adjustments for one or more of the performance metrics.

21. The method of claim 20, wherein calculating comprises calculating to account for losses due to one or more of error correction codes used for data transmission, channel estimation errors at a receiver, variations in interference observed by the receiver, variations in transmit power used for data transmission, or a combination thereof.

22. An apparatus, comprising:

means for determining performance metrics for a plurality of ranks, each rank indicating a different number of data streams to be simultaneously transmitted via a multiple-input multiple-output (MIMO) channel;

means for applying an adjustment to the performance metrics of the plurality of ranks to obtain an adjusted performance metric, the adjustment accounting for system losses; and

means for selecting a rank from the plurality of ranks for data transmission based on the adjusted performance metric.

23. The apparatus of claim 22, wherein the means for determining performance metrics comprises means for determining performance metrics for one or more of capacity of the MIMO channel, throughput of data transmissions sent via the MIMO channel, or signal quality of the MIMO channel.

24. The apparatus of claim 22, further comprising means for calculating adjustments for one or more of the performance metrics.

25. The apparatus of claim 24, wherein the means for calculating comprises means for calculating to account for losses due to one or more of error correction codes used for data transmissions, channel estimation errors at a receiver, variations in interference observed by the receiver, variations in transmission power used for data transmissions, or a combination thereof.

26. A processor-readable medium for storing instructions, the instructions comprising:

instructions for determining performance metrics for a plurality of ranks, each rank indicating a different number of data streams to be simultaneously transmitted via a multiple-input multiple-output (MIMO) channel;

instructions for applying an adjustment to the performance metrics of the plurality of ranks to obtain an adjusted performance metric, the adjustment accounting for system losses; and

instructions for selecting a rank from the plurality of ranks for data transmission based on the adjusted performance metric.