US 7,602,837 B2Grant

Beamforming for non-collaborative, space division multiple access systems

Issue Date:2009-10-13

•20 Claims

•9 Drawing Sheets

Abstract

A wireless communication system noncollaborative, multiple input, multiple output (MIMO) space division multiple access (SDMA) system determines subscriber station combining and weighting vectors that yield a high average signal-to-interference plus noise ratio (SINR). Each subscriber station independently transmits information to a base station that allows the base station to determine a weight vector w_ifor each subscriber station using the determined combining vector of the subscriber station. The i^thcombining vector corresponds to a right singular vector corresponding to a maximum singular value of a channel matrix between a base station and the i^thsubscriber station. Each subscriber station transmits signals using a weight vector v_i, which corresponds to a left singular vector corresponding to a maximum singular value of a channel matrix between the i^thsubscriber station and the base station. The base station uses the weight vector w_ito determine the signal transmitted by the i^thsubscriber station.

Metadata

Assignee

Freescale Semiconductor, Inc.

Inventors

Jayesh H. Kotecha
Jayakrishnan C. Mundarath

Application Information

Application Number:US 11/254,392

Filing Date:2005-10-20

Priority Date:2005-10-20

Art Unit:2611

Classifications

IPC:

H04B1/69H04B7/02

Patent Drawings (9 sheets)

Description

Background of the Invention

[0001] 1. Field of the Invention

[0002] The present invention relates in general to the field of information processing, and more specifically to a system and method for beamforming for non-collaborative, space division multiple access systems with transmitter and receiver antenna arrays.

[0003] 2. Description of the Related Art

[0004] The demand for wireless communication systems continues to expand. Wireless communication systems transmit and receive signals within a designated electromagnetic frequency spectrum. The capacity of the electromagnetic frequency spectrum is limited. Thus, the usage expansion of wireless communication systems continually introduces challenges to improve spectrum usage efficiency. Space division multiple access (SDMA) represents one approach to improving spectrum usage efficiency. SDMA has recently emerged as a popular technique for the next generation communication systems. SDMA based methods have been adopted in several current emerging standards such as IEEE 802.16 and the 3rd Generation Partnership Project (3GPP).

[0005] FIG. 1 depicts a wireless communication system 100 that employs SDMA. The communication system 100 is a multiple-input multiple-output (MIMO) system. In MIMO systems, transmitters and receivers are both equipped with multiple antennas. The wireless communication system 100 includes multiple base stations (BS's) 102.1 through 102.p and multiple subscriber stations (SS's) 104.1-104.r, where “p” and “r” are integers representing the number of base stations and subscriber stations, respectively, in a given geographic area. Base stations and subscriber stations can be both transmitters and receivers when both base stations and subscriber stations are equipped with a receiver and a transmitter. Base stations generally communicate with multiple subscriber stations. Subscriber stations communicate directly with a base station and indirectly, via the base station, with other subscriber stations. The number of base stations depends in part on the geographic area to be served by the wireless communication system 100. Subscriber systems can be virtually any type of wireless one-way or two-way communication device such as a cellular telephones, wireless equipped computer systems, and wireless personal digital assistants. The signals communicated between base stations and subscriber stations can include voice, data, electronic mail, video, and other data, voice, and video signals.

[0006] In a MIMO system, each base station 102 and subscriber station 104 includes an array of antennas for transmitting and receiving signals. SDMA-MIMO wireless communication systems utilize a base station with an array of multiple antennas to transmit to and receive signals from subscriber stations. The antenna array forms a beam by applying a set of weights to signals applied to each antenna in the antenna array. A different set of beam forming weights is applied to communications between the base station and each subscriber station with a goal of minimizing interference between the radio communication devices signals. In some transmission schemes, such as time division duplex (TDD), beam forming between the base station and subscriber stations allows the allocation of the same frequency channel and different time channel to subscriber stations during downlink and uplink. In other transmission schemes, such as frequency division duplex (FDD), beam forming between the base station and subscriber stations allows the allocation of the same time channel and different frequency channel to subscriber stations during downlink and uplink. In SDMA, separation between different subscriber stations sharing the same time-frequency channel occurs in the spatial dimension.

[0007] FIG. 2 depicts base station 202 and subscriber stations 204.1 through 204.m in an SDMA, MIMO wireless communication system. Base station 202 represents each of base stations 102.1 through 102.p, and subscriber stations 204.1 through 204.m represent any group of m subscriber stations. MIMO systems use beamforming to transmit a single data stream through multiple antennas, and the receiver combines the received signal from the multiple receive antennas to reconstruct the transmitted data. In general, “beamforming” processes a signal using weight vector and an array of antennas to direct the signal using interference properties.

[0008] Base station 202 has an array of N antennas 206, where N is an integer greater than or equal to m. The base station prepares a transmission signal, represented by the vector x_i, for each signal s_i, where iε{1, 2, . . . , m}. The transmission signal vector x_iis determined in accordance with Equation [1]:
x_i=w_i·s_i [1]
where w_i, is the i^thbeamforming, N dimensional transmission weight vector (also referred to as a “transmit beamformer”), and each coefficient w_jof weight vector w_irepresents a weight and phase shift on the j^thantenna 206, where jε{1, 2, . . . , k_i}, and k_irepresents the number of receiving antennas of the i^thsubscriber station 204.i. “s_i” is the data to be transmitted to the i^threceiver. The coefficients of weight vector w_iis often a complex weight. Unless otherwise indicated, transmission beamforming vectors are referred to as “weight vectors”, and reception vectors are referred to as “combining vectors”.

[0009] The transmission signal vector x_iis transmitted via a channel represented by a channel matrix H_i. The channel matrix H_irepresents a channel gain between the transmitter antenna array 206 and the i^thsubscriber station antenna array 208.i. Thus, the channel matrix H_ican be represented by a k_i×N matrix of complex coefficients, where k_iis the number of antennas in the i^thsubscriber station antenna array 208.i. The value of k_ican be unique for each subscriber station. The coefficients of the channel matrix H_idepend, at least in part, on the transmission characteristics of the medium, such as air, through which a signal is transmitted. Several conventional methods exist to determine the channel matrix H_icoefficients. In at least one embodiment, a known pilot signal is transmitted to a receiver, and the receiver, knowing the pilot signal, estimates the coefficients of the channel matrix H_iusing well-known pilot estimation techniques. In at least one embodiment, the actual channel matrix H_iis known to the receiver and may also be known to the transmitter.

[0010] Each subscriber station 204 receives signals on the antennas of each subscriber station. The received signals for the i^thsubscriber station 204.i are represented by a k_i×1 received signal vector y_iin accordance with Equation [2]:

[0011]

\begin{matrix} y_{i} = s_{i} H_{i}^{H} w_{i} + (\sum_{n = 1}^{m} s_{n} H_{i}^{H} w_{n} - s_{i} H_{i}^{H} w_{i}) & [2] \end{matrix}

where “s_i” is the data to be transmitted to the i^thsubscriber station 204.i, “s_n” is the data transmitted to the n^thsubscriber station 204.n, “H_i^H” represents the complex conjugate of the channel matrix correlating the subscriber station 204 and i^thsubscriber station 204.i, w_iis the i^thbase station weight vector, and w_nis the n^thbase station 202.n weight vector. The superscript “H” is used herein as a hermitian operator to represent a complex conjugate operator. The j^thelement of the received signal vector y_irepresents the signal received on the j^thantenna of subscriber station 204.i, jε{1, 2, . . . , k_i}. The first term on the right hand side of Equation [2] is the desired receive signal while the summation terms less the desired receive signal represent co-channel interference.

[0012] To obtain a data signal, z_i, which is an estimate of the transmitted data s_i, the subscriber station 204.i combines the signals received on the k antennas using a combining vector v_iin accordance with Equation [3]:
z_i=ŝ_i=v_i^Hy_i [3].

[0013] MIMO-SDMA communication methods can be classified into two major categories: (1) collaborative and (2) non-collaborative. Collaborative MIMO-SDMA methods entail all schemes where the weighting vectors w_iand combining vectors v_iof base station 202 and subscriber station 204.i are designed together in a collaborative fashion, i.e. the knowledge of MIMO channels to all the subscriber stations 204 are used centrally to jointly design the base station 202 weighting and combining vectors and each subscriber station 204. Non-collaborative methods on the other hand employ sequential design, i.e. either the base station 202 or the subscriber stations 204 design their weighting or combining vectors first and knowledge of the designed vectors are used to design the remaining set of vectors.

[0014] The signal throughput capacity of collaborative SDMA systems is conventionally greater than the capacity of non-collaborative systems since collaborative systems benefit from the joint knowledge of the channels H_i, iε{1, 2, . . . m}, to all the subscriber stations 204 while combining vectors for one subscriber station 204.i in the non-collaborative systems are determined independently of the other subscriber stations 204.

[0015] Collaborative systems exhibit downsides including:

- [0016] Feed forward control information—SDMA systems involve feedback of some information from each subscriber station 204i to the base station 202 that allows a base station 202 to know or determine channel information. In collaborative systems, the base station 202 uses this channel information to design both the base station 202 and the subscriber station 204i beamforming weight vectors. The choice of the subscriber station 204.i weight vectors, however, needs to be conveyed to the subscriber station 204.i. Hence this weight vector information needs to be fed-forward to the individual subscriber station 204.i. Non-collaborative schemes, on the other hand, do not feed-forward information.
- [0017] Feedback overhead—Both conventional collaborative and non-collaborative MIMO-SDMA systems require control channels to feedback MIMO channel information to the base station 202. While in the case of collaborative schemes the complete MIMO channel matrix needs to be fed back by each subscriber station 204.i, non-collaborative schemes which design the subscriber station 204.i beamforming combining vectors first need only feed back a vector corresponding to the projection of the subscriber station 204.i choice of a combining vector on to the MIMO channel matrix H_i. This considerably reduces the amount of feedback required with non-collaborative schemes.

[0018] The downsides of collaborative systems can be non-trivial in terms of adversely affecting performance not only in terms of the volume of control information exchanged, but also, for example, in fast changing channel conditions where the cost of an extra bit of control information may cost more than just the size of a bit. Further, in wideband systems, such as orthogonal frequency division multiple access (OFDMA) systems, the feed forward has to be done, in the worst case, on a per subcarrier basis which can significantly increase the overheads of communication.

[0019] However, designing optimal beamforming weight vectors and combining vectors for non-collaborative systems has proven to be an obstacle for conventional systems. To improve signal-to-interference plus noise ratios (SINRs), communication systems attempt to design weight and combining vectors so that transmission signal x_idoes not interfere with any other transmission signal. In a non-collaborative system, if you design the combining vector v_ifirst, the subscriber station 204.i transmits data to the base station so that the base station is aware of the combining vector v_i. The base station 202 then designs the weight vector w_iin light of the combining vector v_i. However, the combining vector v_imight not yield the optimal design for the weight vector w_i. However, the combining vector v_icannot now change, because the weight vector w_iwould become incompatible. The weight vector w_ican be designed first without knowing the combining vector v_i; however, an acceptably high SINR is not guaranteed . Thus, a “catch-22” develops.

Brief Description of the Drawings

[0020] The present invention may be better understood, and its numerous objects, features and advantages made apparent to those skilled in the art by referencing the accompanying drawings. The use of the same reference number throughout the several figures designates a like or similar element.

[0021] FIG. 1 (labeled prior art) depicts a wireless communication system that employs SDMA.

[0022] FIG. 2 (labeled prior art) depicts a base station and subscriber stations in an SDMA, MIMO wireless communication system.

[0023] FIG. 3 depicts a wireless communication system with a base station and subscriber stations.

[0024] FIG. 4 depicts an embodiment of the wireless communication system in FIG. 3.

[0025] FIG. 5 depicts a non-collaborative, SDMA-MIMO downlink communication process.

[0026] FIG. 6 depicts a non-collaborative, SDMA-MIMO uplink communication process.

[0027] FIG. 7 depicts a simulated comparison of the wireless system in FIG. 4 and conventional systems.

[0028] FIG. 8 depicts a simulated comparison of the wireless system in FIG. 4 and conventional systems in the presence of statistical interference.

Detailed Description

[0029] A wireless communication system noncollaborative, multiple input, multiple output (MIMO) space division multiple access (SDMA) system determines subscriber station combining and weighting vectors that yield a high average signal-to-interference plus noise ratio (SINR). Each subscriber station independently transmits information to a base station that allows the base station to determine a weight vector w_ifor each subscriber station using the determined combining vector of the subscriber station. In at least one embodiment, the i^thcombining vector from the i^thsubscriber station is derived from or is generated to be substantially equivalent to a right singular vector corresponding to a maximum singular value of a channel matrix between a base station and the i^thsubscriber station. Each subscriber station transmits signals using a weight vector v_i, and the weight vector v_iis derived from or is generated to be substantially equivalent to a left singular vector corresponding to a maximum singular value of a channel matrix between the i^thsubscriber station and the base station. The base station uses the weight vector w_ito determine the signal transmitted by the i^thsubscriber station. In at least one embodiment, a resulting signal-to-interference plus noise (SINR) improvement results.

[0030] A channel matrix H_ispecifies the transmission channel gain between a transmitter and an i^threceiver. In a noncollaborative, SDMA-MIMO system determining a combining vector v₁in a receiver that corresponds to a right singular vector corresponding to a substantially maximal singular value of channel matrix H₁, and using the combining vector v₁to determine the weight vector used to transmit signals to the receiver can improve the average SINR of the signals.

[0031] FIG. 3 depicts a wireless communication system 300 with a base station 302 and m subscriber stations 304.1 through 304.m. The wireless communication system 300 is a noncollaborative, MIMO-SDMA system. Thus, each base station 302 includes an array of multiple antennas for communicating with the subscriber stations 304.1 through 304.m, and each subscriber station includes respective antenna arrays for communicating with the base station 302. The number of antennas in the antenna arrays is station dependent. Preferably, the base station 302 includes at least as many antennas as the number of subscriber stations.

[0032] In at least one embodiment of wireless communication system 300, all of the m subscriber stations 304 include an independent combining vector v determination module 306 that independently determines respective combining vectors from an associated channel matrix H. In other embodiments, a subset of the m subscriber stations includes the independent combining vector v determination module 306. The i^thsubscriber station 304.i in wireless communication system 300 determines a combining vector v_ifrom the channel matrix H_iindependently, without reference to any channel or weighting information from any other subscriber station, base station, or any other external data source. The subscriber station 304.i transmits information to the base station 302 that allows the base station to generate a weighting vector w_ifor use in transmitting signal s_ito the subscriber station 304.i. The information transmitted to the base station 302 can be any information that allows the base station 302 to obtain or derive the combining vector v_iand to generate the weighting vector w_i. For example, when the same channel matrix is used to transmit and receive, such as in a time division duplex (TDD) system, the subscriber station 304.i can transmit the combining vector v_i. The base station receives H_iv_i, and, knowing H_i, can derive the combining vector v_iand determine weighting vector w_i.

[0033] In another embodiment, the channel matrices used for transmitting and receiving are different (e.g. H_iTand H_iR, from the i^thsubscriber station's perspective), such as in a frequency division duplex (FDD) system. For the subscriber station 304.i to receive and the base station 302 to transmit, the subscriber station 304.i can, for example, feed back the combining vector v_iand channel matrix H_iReither separately or as a product to the base station 302. In at least one embodiment, the base station 302 can estimate the channel matrix H_iTwhen the subscriber station 304.i transmits the product H_iT·v_iand/or the subscriber station 304.i transmits a known pilot sequence using vector v_i. The base station 302 receives v_iand channel matrix H_iR, either separately or as a product, and, thus, can determine the combining vector w_i. In another embodiment, codes can be used to identify predetermined combining vectors. In at least one embodiment, the independent determination of the combining vector v_iand subsequent determination of the base station weight vector w_iusing the combining vector v_iresult in an optimal average SINR over a period of time.

[0034] FIG. 4 depicts an embodiment of wireless communication system 300 in more detail. The wireless communication system 400 includes a base station 402 with an antenna array 406 of N antennas. The wireless communication system 400 also includes m different subscriber stations 404.1 through 404.m, each with an antenna array 408.1 through 408.m. The number of antennas in each subscriber station antenna array can vary between subscriber stations. The MIMO channel from the base station 402 to the i^thsubscriber station 404.i is denoted by H_i, iε{1, 2, . . . , m}. The channel matrix H_iis an N×k_imatrix of complex entries representing the complex coefficients of the transmission channel between each transmit-receive antenna pair, where N represents the number of base station 402 antennas, and k_irepresents the number of antennas of the i^thsubscriber station.

[0035] A non-collaborative, SDMA-MIMO communication process between base station 402 and subscriber stations 404.1 through 404.m can be conceptually separated into an uplink process and a downlink process. In a downlink process, the base station 402 is the transmitter, N equals the number of antennas used for transmitting on the base station 402, and k_irepresents the number of antennas of the i^thsubscriber station 404.1 used to receive the transmitted signal. In an uplink process, the subscriber station 404.i is the transmitter, and the base station 402 is the receiver.

[0036] In a downlink process, the vector v_idetermination module 410.i determines a combining vector v_ifor combining the signals received by each of the k_iantennas of subscriber station 404.i. The coefficients of vector y_irepresent each of the signals received by each of the k_iantennas of subscriber station 404.i. In an uplink process, the vector v_idetermination module 410.i also determines a beamforming weighting vector v_ifor transmitting a signal from subscriber station 404.i to base station 402. In at least one embodiment, base station 402 and each of subscriber stations 404.1-404.m include a processor, software executed by the processor, and other hardware that allow the processes used for communication and any other functions performed by base station 402 and each of subscriber stations 404.1-404.m.

[0037] The uplink channel and the downlink channel may be the same or different depending upon the choice of communication scheme. For example, the uplink and downlink channels are the same for time division duplex (TDD) communication schemes and different for frequency division duplex (FDD) schemes.

[0038] FIG. 5 depicts a non-collaborative, SDMA-MIMO downlink communication process 500 that represents one embodiment of a downlink communication process between base station 402 and subscriber stations 404.1 through 404.m. Referring to FIGS. 4 and 5, in operation 502, the base station 402 transmits a pilot signal to each of subscriber stations 404.1-404.m. After reception of the pilot signal by the subscriber stations 404.1-404.m, using a pilot-based channel estimation technique, subscriber stations 404.1-404.m can respectively estimate channel matrices Ĥ₁through Ĥ_m, where the “^” symbol indicates an estimated value. Pilot-based channel estimation techniques are well-known in the art.

[0039] In operation 506, for all i, vector v_idetermination module 410.i of the i^thsubscriber station 404.i uses the estimated channel matrix Ĥ_ito determine a combining vector v_i, iε{1, 2, . . . , m}. At least in the absence of interference generated by sources other than base station 402 and subscriber stations 404.1-404.m (“external noise interference”), the combining vector v_icorresponds to the right singular vector corresponding to the maximal singular value of the estimated channel matrix Ĥ_i. The right singular vector corresponding to the maximal singular value of the estimated channel matrix Ĥ_ican be determined from the maximum singular value decomposition of channel matrix Ĥ_i. In at least one embodiment, the combining vector v_iequals the right singular vector corresponding to the maximal singular value of the estimated channel matrix Ĥ_ias indicated in Equation [4]:
v_i=v_SVD(rt)=SV_max(Ĥ_i)_right [4].

[0040] The singular value decomposition of matrix Ĥ_iis determined using Equation [5]:
Ĥ_i=UDV^H [5].
where the N×k_imatrix D is a diagonal matrix that contains singular values on the diagonal and zeros off the diagonal, the matrix U is an N×N unitary matrix, and the matrix V is a k_i×k_iunitary matrix whose columns are the right singular vectors for the corresponding singular value in matrix D.

[0041] Thus, in accordance with Equations [4] and [5], the combining vector v_iis the vector from the column in V corresponding to the maximum diagonal value in matrix D.

[0042] In at least one embodiment, the i^thcombining vector from the i^thsubscriber station is derived from or is generated to be substantially equivalent to a right singular vector corresponding to a maximum singular value of a channel matrix between a base station and the i^thsubscriber station. The combining vector v_icorresponding to the right singular vector corresponding to the maximal singular value of the estimated channel matrix Ĥ_ican be determined using other processes. For example, the combining vector v_icorresponding to the right singular vector corresponding to the maximal singular value of the estimated channel matrix Ĥ_icould be determined from the right singular vector corresponding to a non-maximal singular value of the estimated channel matrix Ĥ_iand using one or more factors to modify the result to at least substantially obtain v_SVD(rt).

[0043] In at least one embodiment, the i^thcombining vector is designed in an environment where the channels H_i, iε{1, 2, . . . , m}, between the base station 402 and each subscriber station 404 are statistically independent of one another. This statistical independence represents the general case since any time the base station 402 would not select subscriber stations to share an SDMA burst profile if there is insufficient channel separation between the subscriber stations.

[0044] When external, statistical interference is present, the choice of the combining vector that will yield an improved SINR is determined using a comparison of the SINR from at least two combining vectors. In at least one embodiment, external statistical interference refers to interference whose characteristics can be estimated statistically. In the presence of external, statistical interference, the i^threceiving subscriber station uses available information about the interference to determine the combining vector v_i. The vector v_idetermination module 410.i determines which combining vector provides the best SINR. In at least one embodiment, two types of interference are considered. The first type is instantaneous interference with an instantaneous interference measure b_I. The instantaneous interference measure b_Iis a k×1 vector with the j^thentry in the b_Irepresenting instantaneous, external noise on the j^thantenna jε{1, 2, . . . , k}. The second type of interference is statistical interference with an average, external interference represented by a zero mean with covariance matrix R_I.

[0045] In at least one embodiment, vector v_idetermination module 410.i chooses v_i=v_SVDas defined by Equation [4] during at least a first period of time and chooses v_i=v_{null(I or S)}during at least a second period of time depending upon whether v_SVDor v_nullprovide a better SINR, wherein the subscripts “I” and “S” respectively signify vectors determined for instantaneous and statistical interference. For instantaneous interference, for C>0 v_i=v_nullI, and otherwise v_i=v_SVD, where C for instantaneous interference is defined in at least one embodiment by Equation [6]:

[0046]

\begin{matrix} C = { H_{1} v_{nullI} }^{2} - \frac{σ_{n}^{2}}{σ_{n}^{2} + \frac{1}{k} { b_{I} }^{2}} { H_{1} v_{SVD} }^{2}, & [6] \end{matrix}

where:

[0047]

\begin{matrix} T^{H} = Null (b_{I}); \\ v_{nullI} = T \cdot {SV}_{\max} (T^{H} H_{1}^{H} H_{1} T); \\ v_{SVD} = {SV}_{\max} (H_{i}); and \end{matrix}

[0048] σ_n²represents noise variance measured during a time of no transmission. T^Hequals the complex conjugate of the null space of vector b_I. Vector b_Iis an N dimensional vector representing instantaneous interference. The null space of matrix T is, thus, the set of N−1 vectors which satisfy T^Hb_I=0.

[0049] The left entry on the right hand side of Equation [6] represents the signal-to-noise ratio (SNR) obtained using v_nullI, and the right entry represents the SNR obtained using vector v_SVD.

[0050] For statistical interference, for C>0 v_i=v_nullS, and otherwise v_i=v_SVD, where C for statistical interference is defined in at least one embodiment by Equation [7]:

[0051]

\begin{matrix} C = { H_{1} v_{nullS} }^{2} - \frac{σ_{n}^{2}}{σ_{n}^{2} + \frac{1}{k} { tr (R_{I}) }^{2}} { H_{1} v_{SVD} }^{2}; & [7] \end{matrix}

where:
T=T=R_I^1/2=UΣ^1/2;
v_nullI=T·SV_max(T^HH₁^HH₁T);
v_SVD=SV_max(H_i);
R_I=R_I=UΣU^H, which is the eigen value decomposition of covariance matrix R_I, and covariance matrix R_Irepresents statistical interference, zero mean,
R_I^1/2=UΣ^1/2,
tr(R_I) is the trace matrix of matrix R_I, and

[0052] σ_n²represents noise variance measured during a time of no transmission.

[0053] The left entry on the right hand side of Equation [7] represents the signal-to-noise ratio (SNR) of vector v_nullS, and the right entry represents the SNR of vector v_SVD.

[0054] In operation 508, once the combining vector v_iis determined, the subscriber station 404.i transmits information to the base station 402 that allows the base station 402 to generate a weight vector w_ithat is complimentary to the combining vector v_iand, thus, at least in the absence of external interference, provides a SINR improvement over conventional systems. As described above, in at least one embodiment, when the same channel matrix H is used to transmit and receive, such as in a TDD system, the subscriber station 404.i transmits the combining vector v_ito the base station 404 via channel H_iThe base station receives H_iv_i, and, knowing H_i, can derive the combining vector v_iand determine a complimentary weighting vector w_ias subsequently described. In another embodiment, when the channel matrices used for transmitting and receiving are different (e.g. H_iTand H_iR, from the i^thsubscriber station's perspective), such as in an FDD system, in at least one embodiment, the subscriber station 304.i can transmit the combining vector v_iand channel matrix H_iR. The base station 302 receives H_iTH_i/Rv_i, and, thus, can determine the combining vector w_ifrom H_iTH_i/Rv_i. In another embodiment of FDD, the base station 302 can determine an estimate of the channel matrix H_iRand H_iTin for example, a well-known manner, and the subscriber station 304.i transmits only the combining vector v_i. The base station 302 can then determine the combining vector from H_iTv_i. In another embodiment, codes correlated to a set of predetermined combining vectors or codes representing the combining vector v_ican be used to determine combining vector v_i. In a non-collaborative system, the vector v_idetermination module 410.i determines the i^thcombining vector v_iindependently of the weight vector w_iof base station 402 and independently of the combining vectors of any other subscriber station.

[0055] In operation 510, the base station 402 determines the transmit beamforming weight vector w_ithat is complimentary to combining vector v_i. The base station 402 determines the weight vector w_iwith the goal of eliminating cross-channel interference. In a normalized context, the cross-channel interference can be eliminated by designing the complimentary weight vector w_iusing combining vector v_iin accordance with Equation [8]:

[0056]

\begin{matrix} w_{i}^{H} H_{j} v_{j} = {\begin{matrix} 1 & If i = j \\ 0 & If i \neq j \end{matrix} . & [8] \end{matrix}

In at least one embodiment, the weight vector w_iis complimentary to combining vector v_iwhen Equation [8] is satisfied.

[0057] The method used in operation 510 to determine the weight vector w_i, and, thus, spatially separate the subscriber stations 404.1-404.m is a matter of design choice. In at least one embodiment, the linearly constrained minimum variance (LCMV) algorithm is employed at the base station 404 to determine complimentary weight vector w_i.

[0058] Following is a general description of application of the LCMV applied in at least one embodiment of operation 510 to determine the weight vector w_iusing the combining vector v_ifrom subscriber station 404.i. The base station 402 has N antennas and transmits to m subscriber stations 404 where, preferably, m≦N. The complex vector channels seen by the base station 404 to each of the m subscriber stations 404 are represented by h₁, h₂, . . . , h_m, where h_i=Ĥ_iv_i, and X=[h₁, h₂, . . . , h_m].

[0059] A general goal of an SDMA-MIMO communication system is to design a set of m, N-dimensional beamforming vectors w_i, iε{1, 2, . . . , m} corresponding to each subscriber station 404 so that the transmission to one subscriber station has minimal interference with transmission to other subscriber stations while achieving a specified gain to the intended recipient subscriber station. For the sake of simplicity, assume that the specified gain of a signal intended for a subscriber station is unity and that the gains to other subscriber stations are zero to ensure no intra-system interference. Then the design constraint for the weight vectors can be specified in accordance with Equation [9]:
X^HW=D [9]
where D=Im, Im is an m×m identity matrix, and
W=[w₁,w₂, . . . , w_m] [10],
where w_i, iε{1, 2, . . . , m}, represents the weight vector used for beamforming transmission to the i^thsubscriber station 404.i. Equation [9] can be posed as an LCMV problem in the following manner:

[0060]

\begin{matrix} w_{i} = \arg \min_{w} (w^{H} w) & [11] \end{matrix}

such that:
X^Hw=e_i [12]
where e_iis the all-zero column vector except for the i^thentry which is equal to one.

[0061] The LCMV solution solves a least squares problem which is the minimum transmit power solution for the signal transmitted to subscriber station 404.i while meeting the given gain and interference constraints. Another way to view the LCMV solution is to look at the signal-to-noise ratio (SNR) obtained with unit (normalized) transmit power. If the signal power is σ_s²and the noise power is σ_n², the SNR obtained for subscriber station 404.i for weight vector w_iis given by:

[0062]

\begin{matrix} {SNR}_{i} = \frac{1}{w_{i}^{H} w_{i}} \frac{σ_{s}^{2}}{σ_{n}^{2}} . & [13] \end{matrix}

[0063] The LCMV solution maximizes the SNR_ithat can be obtained by subscriber station 404.i with a fixed transmit power (normalized to one (1) in this case) under the given constraints. In at least one embodiment, differential gains/SNR to different subscriber stations can be ensured by setting different values for the elements of the diagonal matrix D in Equation [9].

[0064] In at least one embodiment of operation 512, the base station 402 transmits m different signals to the m subscriber stations 404.1-404.m on the same time-frequency channel. The modulated data to be transmitted to subscriber station 404.i is denoted by s_i. Each of the m signals s₁through s_mare transmitted through all the N base station 402 antennas 408 using unique complex antenna weights w₁through w_m. In at least one embodiment, the actual signal transmitted on each base station 402 antenna is a superposition of vectors x₁through x_m, where x_i=s_iw_iand iε{1, 2, . . . , m}.

[0065] Subscriber station 404.1 has k₁antennas in antenna array 406.1. In operation 514, the subscriber station 404.1 receives signal vector y₁. In at least one embodiment, for subscribers station 404.1, signal vector y₁is defined by Equation [14]:

[0066]

\begin{matrix} y_{1} = s_{1} {\hat{H}}_{1}^{H} w_{1} + \sum_{i = 2}^{m} s_{i} {\hat{H}}_{1}^{H} w_{i} + n, & [14] \end{matrix}

where “s₁” the data to be transmitted to subscriber station 404.1, “Ĥ₁^H” represents the complex conjugate of the estimated channel matrix Ĥ_l, w_iis the i^thbeamforming, N dimensional weighting vector, and the vector n represents external noise interference for iε{1, 2, . . . , m}. The superscript “H” is used herein to represent a complex conjugate operator. The j^thelement of the received signal vector y_irepresents the signal received on the j^thantenna of subscriber station 404.i, jε{1, 2, . . . , k}. Equation [14] can be used for all y_iby letting the first term on the right hand side of RHS of Equation [14] be the desired receive signal while the summation terms represent co-channel interference.

[0067] The subscriber station 404.i then weights and sums the receive signal vector y_iusing the combining vector v_iused by base station 402 to generate w_ito determine the desired output data signal z_i, which is an estimate of the transmitted data signal s_i, in accordance with Equation [15]:
z_i=ŝ_i=v_i^Hy_i [15].

[0068] FIG. 6 depicts a non-collaborative, SDMA-MIMO uplink communication process 600 that represents one embodiment of an uplink communication process between base station 402 and subscriber stations 404.1 through 404.m. In operation 602, the base station determines an estimate of the uplink channel matrix Ĥ₁ if the uplink channel matrix Ĥ₁ is not already known to the base station 402. In some communication processes, such as the TDD process, the estimated uplink channel matrix Ĥ₁ corresponds directly to the estimated downlink channel matrix Ĥ_i. If the base station 402 does not know the uplink channel process, in one embodiment, operation 602 determines uplink channel matrix Ĥ₁ in the same manner as operation 502 except that the roles of the subscriber stations 404.1-404.m and the base station 402 are reversed.

[0069] In operation 604, during transmission by subscriber station 404.i, vector v_idetermination module 410.i determines a weight vector v_i. The weight vector v_icorresponds to the left singular vector corresponding to the maximal singular value of the k_i×N estimated uplink channel matrix Ĥ_i as indicated by Equation [16]:

[0070]

\begin{matrix} \underline{v_{i}} = v_{SVD (left)} = {{SV}_{\max} (\underline{\hat{H_{i}}})}_{left} . & [16] \end{matrix}

[0071] The singular value decomposition of matrix H_iis determined using Equation [17]:
Ĥ_i=UDV^H [17].
where the N×k_imatrix D is a diagonal matrix that contains singular values on the diagonal and zeros off the diagonal, the matrix U is an N×N unitary matrix whose columns are the left singular vectors for the corresponding singular value in matrix D, and the matrix V is a k_i×k_iunitary matrix.

[0072] Thus, in accordance with Equations [16] and [17], the weight vector v_i is the vector from the column in U corresponding to the maximum diagonal value in matrix D.

[0073] In at least one embodiment, the i^thweighting vector from the i^thsubscriber station is derived from or is generated to be substantially equivalent to a left singular vector corresponding to a maximum singular value of a channel matrix between a base station and the i^thsubscriber station. In at least one embodiment, the weight vector v_i corresponding to the left singular vector corresponding to the maximal singular value of the estimated channel matrix Ĥ_i can be determined using other processes. For example, the weight vector v_i corresponding to the left singular vector corresponding to the maximal singular value of the estimated channel matrix Ĥ_i could be determined from the left singular vector corresponding to a non-maximal singular value of the estimated channel matrix Ĥ_i and using one or more factors to modify the result to at least substantially obtain v_SVD(left).

[0074] In operation 606, subscriber station 404.i sends a signal s_iv_ito base station 402.

[0075] In operation 518 for TDD, the base station estimates s_iusing weight vector w_i, which is the same as the weight vector w_iused during the downlink process, Assuming that the received signal is the vector y_i, signal vector y_iis defined by Equation [1 8]:

[0076]

\begin{matrix} \underline{y_{1}} = s_{1} \underline{{\hat{H}}_{1}^{H} v_{1}} + \sum_{i = 2}^{m} s_{i} \underline{{\hat{H}}_{1}^{H} v_{n}} + n; & [18] \end{matrix}

[0077] where “s₁” the data to be transmitted to base station 402, “Ĥ_i^H” represents the complex conjugate of the estimated channel matrix Ĥ_i, v_iis the beamforming weight vector of subscriber station 404.1 i^thbeamforming, N dimensional weighting vector, and n represents external noise interference for iε{1, 2, . . . , m}. The superscript “H” is used herein to represent a complex conjugate operator. The j^thelement of the received signal vector y_irepresents the signal received on the j^thantenna of base station, jε{1, 2, . . . , N}. The first term on the right hand side of RHS of Equation [14] is the desired receive signal while the summation terms represent co-channel interference.

[0078] In operation 608, the base station 404 then weights and sums the receive signal vector y_iusing the weight vector w_iform the desired output data signal, z_i, that estimates the transmitted signal s_iin accordance with Equation [15]:
z_i=ŝ_i=w_i^Hy_i [19].

[0079] FIG. 7 depicts a simulated comparison 700 between wireless communication in wireless communication system 400 using non-collaborative, SDMA-MIMO communication process 500 and conventional maximal ratio combining (MRC) processes. For the simulation, the number of base station transmit antennas N=5, and, for each subscriber station, the number of receive antennas k=2. The results are shown for a variable number of subscriber stations. The curve 702 depicts the SNR achieved using non-collaborative, SDMA-MIMO communication process 500. The curve 704 depicts the SNR achieved using MRC, and the curve 707 depicts the SNR achieved using MRC with maximum SINR. The curve 702 depicts a 3-4 dB gain when transmitting to multiple subscriber stations.

[0080] FIG. 8 depicts a simulated comparison 800 between wireless communication in wireless communication system 400 when determining v_iin the presence of external statistical interference and conventional maximal ratio combining (MRC) processes. For the simulation, the number of base station transmit antennas N=5, and, for each subscriber station, the number of receive antennas k=2. The results are shown for a variable number of subscriber stations. The curve 702 depicts the SNR achieved using non-collaborative, SDMA-MIMO process of determining v_iin the presence of statistical interference. The curve 804 depicts the SNR achieved using MRC, and the curve 808 depicts the SNR achieved using MRC with maximum SINR. The curve 802 depicts a 3-4 dB gain.

[0081] Although the present invention has been described in detail, it should be understood that various changes, substitutions and alterations can be made hereto without departing from the spirit and scope of the invention as defined by the appended claims.

Claims

What is claimed is:

1. A wireless communication method using beamforming for a noncollaborative, multiple input, multiple output (MIMO) space division multiple access (SDMA) system, the method comprising:

receiving signals on k multiple antennas of a first device, wherein “k” is an integer greater than one (1);

during at least a first period of time, combining the signals using a combining vector v₁, wherein the combining vector v₁corresponds to a right singular vector corresponding to a substantially maximal singular value of channel matrix H₁, wherein H₁represents a channel between the first device and a second device;

during at least the first period of time, determining a data signal using the combining vector v₁; and

transmitting information to a second device that allows the second device to use the combining vector v₁to design a weight vector.

2. The method of claim 1 further comprising:

transmitting a signal to the second device using a weight vector v₁, wherein the weight vector v₁ corresponds to a left singular vector corresponding to the maximal singular value of an uplink channel matrix H_i.

3. The method of claim 1 further comprising:

determining whether the combining vector v₁or a combining vector v_Aprovides better signal-to-interference plus noise ratio; and

during at least a second period of time, determining the data signal using the combining vector v₁if the combining vector v₁provides a better signal-to-interference plus noise ratio and otherwise determining the combined signal using the combining vector v_A.

4. The method of claim 3 wherein the combining vector v_Ais determined based on statistical interference and instantaneous interference.

5. The method of claim 1 further comprising:

determining the combining vector v₁using a processor of the first device.

6. The method of claim 1 further comprising:

determining the combining vector v₁independently of combining vectors and channel matrices of all other devices in the MIMO-SDMA system.

7. The method of claim 1 wherein y₁is a k element reception vector for the first device, w_l^His an N element, conjugate transpose of a signal weighting vector w₁, y₁=s₁Ĥ₁^Hw₁+interference, z₁represents the combined signal, and determining the data signal using the combining vector v₁comprises:

determining a dot product of a conjugate transpose of the combining vector v₁and the reception vector y₁such that z₁=v₁^Hy₁and “z₁” represents the data signal.

8. The method of claim 1 wherein the information transmitted to the second device allows the second device to use the combining vector v₁to generate a weight vector for transmission of signals from the second device to the first device.

9. The method of claim 1 wherein the first device is selected from the group comprising cellular telephones, wireless equipped computer systems, and wireless personal digital assistants.

10. A subscriber station comprising:

a receiver to receive beam formed signals on k antennas of a first receiver, wherein “k” is an integer greater than one;

a module to combine the signals using a combining vector v₁to determine a data signal, wherein during at least a first period of time, the combining vector v₁corresponds to a right singular vector corresponding to a substantially maximal singular value of channel matrix H₁, wherein H₁represents a channel between the first receiver and a base station; and

a transmitter to transmit information to a multiple antenna base station that allows the base station to use the combining vector v₁to design a weight vector.

11. The subscriber station of claim 10 wherein the transmitter is further capable of transmitting a signal to the base station using a weight vector v₁, wherein the weight vector v₁ corresponds to a left singular vector corresponding to the maximal singular value of an uplink channel matrix H_i.

12. The subscriber station of claim 10 wherein channel matrix H₁is an estimate of an actual channel matrix, and the coefficients of channel matrix H_irepresent channel gains.

13. The subscriber station of claim 10 further comprising:

a determination module to determine whether the combining vector v₁or a combining vector v_Aprovides better signal-to-interference plus noise ratio and to determine, during at least a second period of time, the data signal using the combining vector v₁if the combining vector v₁provides a better signal-to-interference plus noise ratio and otherwise determining the combined signal using the combining vector v_A.

14. The subscriber station of claim 13 wherein the combining vector v_Ais determined based on statistical interference and instantaneous interference.

15. The subscriber station of claim 10 further comprising:

a module to determine the combining vector v₁using a processor of the first receiver.

16. The subscriber station of claim 10 further comprising:

a module to determine the combining vector v₁independently of combining vectors and channel matrices of all other subscriber stations.

17. The subscriber station of claim 10 wherein y₁is a k element reception vector for the first receiver, w_l^His an N element, conjugate transpose of a signal weighting vector w₁, y₁=s₁Ĥ₁^Hw₁+interference, and to determine the data signal using the combining vector v₁, the module to combine the signals is further capable of:

determining a dot product of a conjugate transpose of the combining vector v₁and the reception vector y₁such that z₁=v₁^Hy₁, wherein “z₁” represents the data signal.

18. The subscriber station of claim 10 wherein the information transmitted to the base station that allows the base station to use the combining vector v₁comprises data from which the combining vector v₁can be derived.

19. The subscriber station of claim 10 wherein the subscriber station is selected from the group comprising cellular telephones, wireless equipped computer systems, and wireless personal digital assistants.

20. A wireless communication method using beamforming for a noncollaborative, multiple input, multiple output (MIMO) space division multiple access (SDMA) system, the method comprising:

determining a weight vector v₁ corresponding to a left singular vector corresponding to a substantially maximal singular value of channel matrix H₁, wherein H₁represents a channel between a first device and a second device;

transmitting information to the second device from the first device that allows the second device to use a combining vector v₁corresponding to a right singular vector corresponding to a substantially maximal singular value of channel matrix H₁to design a weight vector w₁; and

transmitting a signal to the second device using the weight vector v₁.

Patent Citations (4)

Patent	Date	Inventor	Cited By
US5909649(A)	1999-06-01	Saunders	Applicant
US2006/0155798(A1)	2006-07-01	Ketchum et al.	Examiner
US2007/0223619(A1)	2007-09-01	Lee et al.	Examiner
US2007/0286304(A1)	2007-12-01	Kim et al.	Examiner

Non-Patent Literature (12)

D.J. Love et al., “Grassmannian Beamforming for Multiple-Input Multiplt-Output Wireless Systems,” IEEE, 2003.Applicant
L. Dong et al., “Opportunistic Transmission Scheduling for Multiuser MIMO Systems,” IEEE, 2003.Applicant
H. Shin et al., “Closed-form Formulas for Ergodic Capacity of MIMO Rayleigh Fading Channels,” IEEE, 2003.Applicant
M. Torlak et al., “Fast Estimation of Weight Vectors to Optimize Multi-Transmitter Broadcast Channel Capacity,” IEEE Transactions on Signal Processing, vol. 46, No. 1, Jan. 1998.Applicant
V. Zaharov et al., “Smart Antenna Application for Satellite Communication Systems with Space Division Multiple Access,” Journal of Radio Electronics, N. 2, Feb. 5, 2001, printed Oct. 18, 2005.Applicant
M. Cooper et al., “Intelligent Antennas: Spatial Division Multiple Access,” 1996 Annual Review of Communications.Applicant
G.J. Foschini et al., On Limits of Wireless Communication in a Fading Environment when Using Multiple Antennas, Wireless Personal Communications, vol. 6, 1998, pp. 311-335. http://www1.bell-labs.com/project/blast/wpc-v6n3.pdf.Applicant
S.V. Bana et al., “Space Division Multiple Access (SDMA) for Robust Ad hoc Vehicle Communication Networks,” The IEEE Fourth International Conference on Intelligent Transportation Systems, 2001 IEEE Intelligent Transportation Systems.Applicant
D.J. Love et al., “Grassmannian Beamforming on Correlated MIMO Channels,” IEEE Global Telecommunications Conference, Nov. 3 - Dec. 3, 2004.Applicant
A. Bourdoux et al., “Joint TX-RX Optimisation for MIMO-SDMA Based on a Null-space Constraint,” 2002 IEEE 56th Vehicular Technology Conference.Applicant
R. Monzingo et al., Introduction to Adaptive Arrays, Chapter 3, John Wiley & Sons, 1980, pp. 78-151.Applicant
A.F. Anguib et al., Performance of Wireless CDMA with M-ary Orthogonal Modulation and Cell Site Antenna Arrays, IEEE, Journal on Selected Areas in Communications, vol. 14, No. 9, Dec 1996.Applicant