Abstract:
An embodiment of the present invention provides a method, comprising using an adaptive codebook for beamforming for communications in wireless networks.

Description:
CROSS REFERENCED TO RELATED APPLICATIONS 
       [0001]    This application claims the benefit under 35 U.S.C. § 119(e) to U.S. Provisional Patent Application No. 61/093,699, filed Sep. 2, 2008 and is hereby incorporated by reference in its entirety. 
     
    
     BACKGROUND 
       [0002]    In closed-loop multiple input multiple output (MIMO) beamforming, a subscriber station (SS) quantizes the ideal beamforming matrix and sends the quantization index back to a base station (BS). The BS reconstructs the beamforming matrix according to the feed back index and conducts the beamforming. It is well known that the beamforming increases the link performance and system throughput. 
         [0003]    Although the present invention is not limited in this respect, in 802.16e (WiMAX), the ideal beamforming matrix is quantized by a constant quantization codebook. The codebook is optimized for a single channel scenario, where the transmit antenna correlation at the BS is zero. However, the transmit antenna correlation is not constantly zero in reality and varies with several factors such as the antenna spacing at the BS, the BS antenna height, LOS/NLOS condition, BS and SS separation. Furthermore, the optimal quantization codebook varies with the antenna correlation, and thus it is desirable to adapt the codebook to the correlation. For example, the discrete Fourier transform (DFT) codebook and the 802.16e codebook are optimized for either the high or the low antenna correlations but not both. Fortunately, the antenna correlation varies very slowly as compared to the short-term channel fading, and there is a feedback mechanism for long term information in 802.16e. 
         [0004]    Thus, a strong need exists for techniques utilizing adaptive codebooks for beamforming in wireless networks. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0005]    The subject matter regarded as the invention is particularly pointed out and distinctly claimed in the concluding portion of the specification. The invention, however, both as to organization and method of operation, together with objects, features, and advantages thereof, may best be understood by reference to the following detailed description when read with the accompanying drawings in which: 
           [0006]      FIG. 1  illustrates codeword distributions of IEEE 802.16e, DFT, and transformed codebooks; 
           [0007]      FIG. 1   a  provides a quantization constellation for the off-diagonal entry of R t , which has a point at (0,0) for uncorrelated channels of embodiments of the present invention; 
           [0008]      FIG. 2  illustrates packet error rates of three codebooks with low antenna correlation: 802.16e, DFT and an adaptive codebook according to embodiments of the present invention; 
           [0009]      FIG. 2   a  provides a quantization constellation for the off-diagonal entry of R t , which is dedicated for correlated channels of embodiments of the present invention; 
           [0010]      FIG. 3  illustrates packet error rates of three codebooks with high antenna correlation: 802.16e, DFT and the proposed adaptive codebook according to one embodiment of the present invention; and 
           [0011]      FIG. 4  illustrates throughput comparisons of the three codebooks in single-user MIMO and various multiuser MIMO schemes. 
       
    
    
       [0012]    It will be appreciated that for simplicity and clarity of illustration, elements illustrated in the figures have not necessarily been drawn to scale. For example, the dimensions of some of the elements are exaggerated relative to other elements for clarity. Further, where considered appropriate, reference numerals have been repeated among the figures to indicate corresponding or analogous elements. 
       DETAILED DESCRIPTION 
       [0013]    In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be understood by those skilled in the art that the preset invention may be practiced without these specific details. In other instances, well-known methods, procedures, components and circuits have not been described in detail so as not to obscure the present invention. 
         [0014]    Although embodiments of the invention are not limited in this regard, discussions utilizing terms such as, for example, “processing,” “computing,” “calculating,” “determining,” “establishing”, “analyzing”, “checking”, or the like, may refer to operation(s) and/or process(es) of a computer, a computing platform, a computing system, or other electronic computing device, that manipulate and/or transform data represented as physical (e.g., electronic) quantities within the computer&#39;s registers and/or memories into other data similarly represented as physical quantities within the computer&#39;s registers and/or memories or other information storage medium that may store instructions to perform operations and/or processes. 
         [0015]    Although embodiments of the invention are not limited in this regard, the terms “plurality” and “a plurality” as used herein may include, for example, “multiple” or “two or more”. The terms “plurality” or “a plurality” may be used throughout the specification to describe two or more components, devices, elements, units, parameters, or the like. For example, “a plurality of stations” may include two or more stations. 
         [0016]    Embodiments of the present invention provide an adaptive codebook varying with the antenna correlation. Both a BS and a SS transform the 802.16e codebooks synchronously to generate a new quantization codebook optimized for the BS antenna correlation. Simulation results demonstrate that the codebook of embodiments of the present invention uniformly outperforms the competitor codebooks for all antenna correlation. Furthermore, the codebook of the present invention has almost the same performance of the optimal codebooks that are optimized for each given antenna correlation. Finally, the transformation of embodiments of the present invention provides a backward compatible solution to 802.16e and further the 802.16e codebook. 
         [0017]    In closed-loop MIMO beamforming, a subscriber station (SS) quantizes the ideal beamforming matrix and sends the quantization index back to a base station (BS). The BS reconstructs the beamforming matrix according to the feed back index and conducts the beamforming. As mentioned in above, it is well known that the beamforming increases the link performance and system throughput. 
         [0018]    In IEEE 802.16e (WiMAX), the ideal beamforming matrix is quantized by a constant quantization codebook. The codebook is optimized for a single channel scenario, where the transmit antenna correlation at the BS is zero. However, the transmit antenna correlation is not constantly zero in reality and varies with several factors such as the antenna spacing at the BS, the BS antenna height, LOS/NLOS condition, BS and SS separation. Furthermore, the optimal quantization codebook varies with the antenna correlation, and thus it is desirable to adapt the codebook to the correlation. For example, present DFT codebooks and 802.16e codebooks are optimized for either one of the high and low antenna correlations but not both. Fortunately, the antenna correlation varies very slowly as compared to the short-term channel fading. The feedback of the correlation costs little system overhead because (unlike beamforming matrix feedback varying for each subband) it remains the same for the whole frequency band and is fed back infrequently say in every 100 ms. 
         [0019]    Embodiments of the present invention provide an adaptive codebook varying with the antenna correlation. Both BS and SS transform the 802.16e codebooks synchronously to generate a new quantization codebook optimized for the BS antenna correlation. 
         [0020]    The 802.16e codebook is designed for zero antenna correlation, where the ideal beamforming matrix is uniformly distributed. Consequently, the quantization codeword matrix (or vector) is uniformly distributed over quantization spaces. Since the beamforming in an 802.16e system is only for a single user MIMO in long distance (and NLOS) case, the antenna correlation is typically close to zero. However, for WiMAX 2, more transmit antennas (up to 8) and closer antenna mounting (half wavelength) are considered, although the present invention is not limited in this respect. Therefore, the antenna correlation is not zero for some cases while it is close to zero for the others. For example, multi-user MIMO (or downlink SDMA) works well in small antenna spacing while single user MIMO prefers large antenna spacing. 
         [0021]    It is undesirable to define multiple codebooks for the different antenna correlations. Embodiments of the present invention provide a universal solution and can transform codebooks adaptively for all antenna correlations. The transformation is a simple function of the antenna correlation. 
         [0022]    The present inventions adaptive codebook can be viewed from a differential perspective. The antenna correlation matrix varies very slowly and it carries the average beamforming directions of the strongest received signals. Furthermore, the distribution of the instantaneous beamforming directions concentrates at the average beamforming directions as the correlation increases. Instead of uniformly spreading the quantization codewords, embodiments of the present invention transform the uniform codebook so that the codewords concentrate at the average directions for reducing quantization error. 
         [0023]    The idea of the adaptive codebook is illustrated generally as  100  of  FIG. 1  by an example as compared to the other two codebooks. In the example, the channel is a 2x1 real channel with two transmit and one receive antennas. The codewords of the 16e codebook  110  uniformly distribute over a semicircle. In contrast, the DFT codebook  120  only has two valid codewords 
         [0000]    
       
         
           
             
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         [0000]    due to the constant modulus constraint and leaves big holes in the quantization space. For an uncorrelated channel, the input to the quantizer, i.e. the ideal beamforming matrix, uniformly distributes over the semicircle and thus matches the codeword distribution of the 802.16e codebook. For a highly correlated channel, the channel response magnitude of each transmit antenna has almost the same value and therefore the entry magnitude of the ideal beamforming matrix also has almost the same value. This matches the codeword distribution of the DFT codebook. This explains how two codebooks  110  and  120  work for the two extreme scenarios but not both. Besides the magnitude information used in the DFT codebook, the adaptive codebook  130  further exploits the phase information obtained from the antenna correlation. The adaptive codebook  130  transforms the quantization codewords to the preferable direction where the input beamforming matrix will mostly likely be present. The relocated codewords match the input distribution of the beamforming matrix and thus reduce the quantization error. 
         [0024]    Signal Model 
         [0025]    The baseband signal model is given by 
         [0000]        y=HVs+n,   (1) 
         [0026]    where n is the complex AWGN with variance N 0 ; s is the N s  by 1 transmitted vector with unit power; N s  is the number of spatial streams; y is the received vector; H is the channel matrix of size N r  by N t ; V is the beamforming matrix (or vector) of size N t  by N s . In Rayleigh fading channel, the correlated channel matrix H is generated from the channel matrix H w  with independent, identically distributed (i.i.d.) entries as 
         [0000]      H=R r   1/2 H w R t   1/2 ,  (2) 
         [0027]    where R r  is the N r  by N r  receive covariance matrix and R t  is the N t  by N t  transmit covariance matrix. The transmit covariance matrix may be defined as 
         [0000]    
       
         
           
             
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         [0000]    in theory or be simply computed as the empirical mean of H H H over channel matrix samples Hs in practice. Since the SS antenna height is low with respect to the surrounding objects, R r  can be approximated by the identity matrix. Therefore, (2) can be simplified as 
         [0000]      H=H w R t   1/2 .  (3) 
         [0028]    Let the singular value decomposition of R t  be 
         [0000]      R t =QΣ 2 Q H ,  (4) 
         [0029]    where Q is an N t  by N t  unitary matrix i.e. Q H Q=I; Σ is the diagonal matrix with the square roots of the singular values σ i  for i=1, . . . , N t  in decreasing order σ i ≧σ i+1 . Note that the power of R t  in (3) can be written as R t   1/2 =QΣ l Q H . Equation (2) is a simplified channel model of correlated channels, where the correlations at the transmitter and the receiver are decoupled and are modeled by two separate matrixes R r  and R t . For the case where the decoupling is invalid, the correlation matrix of channel matrix entries is defined as R H =E(vec(H)vec(H) H ), where vec(X) stacks the columns of matrix X to make a long column vector. 
         [0030]    In Ricean fading channel, a line-of-sight (LOS) component  H  is added to (2) as 
         [0000]        H=  H +R   r   1/2   H   w   R   t   1/2 .  (5) 
         [0031]    The transmit covariance matrix is defined as 
         [0000]    
       
         
           
             
               
                 
                   
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         [0032]    Codebook Transformation 
         [0033]    Denote the quantization codebook with uniformly distributed codewords as C unif  and its codewords as V i  for i=1, . . . , N c . We would like to transform the uniform codebook to generate a new codebook for the correlated channels. 
         [0034]    The present invention derived the distribution of the ideal beamforming matrix for correlated channels with known R t . Using the derived input distribution, for very high resolution codebooks with many codewords, we can derive the asymptotically optimal transformation that minimizes the channel capacity loss. However, since the transformation requires high complexity functions such as hypergeometric function, it is not practical for the implementation on the mobile device. We simplify the transformation and maintain a performance very close to the optimal. The transformation takes the form of 
         [0000]        {tilde over (V)}   i =orth( FV   i ),  (7) 
         [0000]    where {tilde over (V)} i  is the i-th codeword of the new codebook; orth(X) converts the input matrix (or vector) X to an orthogonal matrix with orthonormal column(s) that span the same subspace as X&#39;s columns; F is a N t  by N t  transformation matrix. orth(X) is essentially the orthogonalization of X and can be simply implemented by various methods such as Grant-Schmidt and QR decomposition. The transformation matrix F may be a function of R H , R t , R r ,  H , Q, Σ, N t , N s , N r , and signal to noise ratio (or N 0 ). In the following we take the unitary preceding as example for explanation but it also can have the same application for non-unitary preceding. 
         [0035]    Embodiments of the present invention provide three representations of transformation matrix F. 
         [0036]    For practicality, F takes the polynomial form 
         [0000]    
       
         
           
             
               
                 
                   
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         [0000]    where  H   H   H  contains the beamforming direction of the LOS components; α 0  may be zero for Rayleigh fading channels; α i  may be a function of the signal to noise ratio (SNR) denoted by γ; K may be an integer e.g. 2 or 4; i is an integer variable. For low SNR, since it is desirable to have a slightly greater contribution from the high order term of R t   i/K  in (6), the weight coefficient α i  may increase as γ decreases e.g. α i =γ −i . For simplicity, a constant expression may be used for all γs per combination of N t , N s , N r . For example, 
         [0000]      F=R t   (9) 
         [0000]    may be used for all cases.
 
The l-th power of R t  has a general form
 
         [0000]      R t   l =QΣQ a ,  (9.1) 
         [0000]    where Q a  can be any N t  by N t  unitary matrix. Because of this, (8) and (9) have various forms for different choices of Q a s. When Q a =Q H , the decomposition of R t  in (4) gives another way to write (8) as 
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         [0000]    where Q contains the global and local maximums of the ideal beamforming directions for Rayleigh fading channels. If the original codebook for the transformation is uniformly distributed, then the rotation of Q H  in (10) and (11) can be dropped i.e. Q a =I and the simplified transformation F is 
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         [0000]    It should be noted that R t   1/2  has a general form R t   1/2 =QΣQ a , where Q a  can be any N t  by N t  unitary matrix. The Cholesky decomposition form of R t   1/2 , which is a triangular matrix, is a special case of the general form. 
         [0037]    In sum, the simplified codebook transformation can be written as 
         [0000]        {tilde over (V)}   i =orth( R   t   l   V   i )  (14) 
         [0000]      or equivalently 
         [0000]        {tilde over (V)}   i =orth( QΣ   2l   V   i ),  (15) 
         [0038]    where Q and Σ are computed from the long term CSI; {tilde over (V)} i  is the transformed codeword; and V i  is codeword of the original codebook. Q consists of the favorable, long term beamforming directions and Σ specifies the concentration to those directions. For implementation simplicity, since l=1 delivers good performance for all cases and it doesn&#39;t require computing the rational power of R t , the simplest form of transformation is 
         [0000]        {tilde over (V)}   i =orth( R   t   V   i ).  (16) 
         [0039]    For feedback reduction, the receiver may not feed back the correlation matrix R t . Instead, it may feed back only part of Q and Σ. For example, it may feed back the first N s  columns of Q and the first N a  eigenvalues of Σ. The transmitter can add complementary and orthogonal columns to the fed back columns to approximate Q and use small values e.g. the minimum of the fed back eigen values to replace the not fed back eigenvalues of Σ. Furthermore, differential technique can be applied to the feedback of R t  or its equivalents. 
         [0040]    In the practical system the suitable representation could be chosen based on the balance between the performance, feedback overhead and complexity. 
         [0041]    The adaptive codebook can be applied to both one-shot and differential feedbacks. The description above is for one-shot feedback. The differential feedback has the form [3][4] 
         [0000]        V ( t+ 1)= Q ( V ( t )) D   i ,  (17) 
         [0042]    where V(t) and V(t+1) are the beamforming matrices at time t and t+1; Q(V(t)) is a square rotation matrix that rotates V(t) to V(t+1) using D i . D i  is selected from a so called “differential codebook” and the index of D i  is fed back from the receiver to the transmitter. The codewords of the differential codebook usually do not uniformly distribute. Instead, they center on somewhere e.g. the identity matrix. The concentration of the codewords increases as the correlation. Therefore, the differential codebook can adapt to the correlation as the one-shot codebook did using (7). A simple example of the transformation is 
         [0000]        {tilde over (D)}   i =orth(Σ l   D   i ),  (18) 
         [0043]    where Σ is computed from the long term CSI; l is some number depending on N t , N s , N r , γ, and mobile speed; {tilde over (D)} i  is the transformed codeword; and D i  is the codeword of the original codebook. 
         [0044]    Variants with Low Complexities 
         [0045]    Since the orth( ) operation increases complexities at the subscriber station, we devise some variants of the original scheme above. The first variant is the simplest. The SS simply removes the orth( ) operation and directly uses the unorthogonalized matrix FV i  to select codeword and feeds back codeword index. 
         [0046]    The second variant is as follows. The SS doesn&#39;t transform the codebook. Instead, it transforms the channel matrix H and uses the uniform codebook (e.g. 16e codebook) to quantize the ideal beamforming matrix of the transformed channel matrix {tilde over (H)} as 
         [0000]      {tilde over (H)}=HT H .  (19) 
         [0047]    When T H =R t   −1/2 , the correlation in H expressed in (3) is removed and {tilde over (H)} becomes uncorrelated, whose distribution matches the uniform codebook. In general, we may let T H =QΣ −l     t    for some positive number l t . The transmitter receives the fed back index and lookups the codeword V i  from the uniform codebook. The codeword of the original codebook is then transformed to the actual one as 
         [0000]        {tilde over (V)}   i =orth( T   H   −H   V   i ).  (15) 
         [0048]    Now, most of the computation burden is shifted to the BS. 
         [0049]    Quantization of Transformation Matrix 
         [0050]    The receiver may feed back the transformation matrix in various forms. For example, it may send back the upper triangle of R t   l     2    for some number l 2  e.g. l 2 =1. A scalar quantization may be applied. For example, when F=R t  is used, since R t  is Hermitian, only the upper (or lower) triangle of R t  is quantized entry by entry. Before quantization, R t  is scaled by the maximum number on the diagonal as 
         [0000]    
       
         
           
             
               
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         [0000]    and the maximum number on the diagonal is normalized to unity. Since the diagonal of R t  is real and positive, 1 quantization bit may be sufficient and the diagonal entry may be quantized to either 1 or 0.8. The off-diagonal entry of R t  is usually complex and requires 4 quantization bits per entry. The 16 points of the quantization constellation may be allocated within the unit circle. One example, although not limited to this example, is shown in  FIG. 1   a , generally as  100   a . The constellation points are located on two circles with radiuses 0.4 and 0.9, respectively. A single point sits at (0,0) and this point is dedicated to the accurate quantization for uncorrelated and slightly correlated channels. In this case, the transformation operation may be always on regardless of the actual channel correlation scenarios, i.e. correlated or uncorrelated. Another non-limiting illustrative example is shown generally as  200   a  of  FIG. 2   a . The constellation is only for correlated channels because the system may only use the original (uniform) codebook and turn off the transformation operation for uncorrelated channels where the transformation can not help. No point is located at (0,0). The points all have norms greater than 0.3. There may be more points at the outer part of the constellation than at the inner part. For a third example, when 
         [0000]    
       
         
           
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         [0000]    is used and 
         [0000]    
       
         
           
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         [0000]    is computed by Cholesky decomposition, similar scalar quantization scheme can be applied to the upper triangle of 
         [0000]    
       
         
           
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               1 
               2 
             
           
         
       
     
         [0000]    that is an upper triangular matrix. 
         [0051]    Simulation Results 
         [0052]    Some of the link level results are shown generally as  200  of  FIG. 2  for a low correlation case and generally as  300  of  FIG. 3  for a high correlation case. The channel model is ITU Pedestrian B. The BS has four transmit antennas and the SS has two receive antennas. The antenna spacings of BS transmit antennas are 4 wavelengths and 0.5 wavelength for practical mounting with low and high correlations. One data stream is transmitted. Modulation and code rate are 64QAM and 0.5. Three codebooks are tested. The first one is a conventional 802.16e codebook  220  (low correlation)  320  (high correlation); the second is the DFT codebook  210  (low correlation)  310  (high correlation); and the third one is the adaptive codebook  230  (low correlation)  330  (high correlation) from embodiments of the present invention. The ideal SVD is shown at  240  (low correlation)  340  (high correlation). For both low and high correlation cases, the proposed adaptive codebook has the best performances. These performances are very close to those of the optimal codebooks. The optimal codebook for zero correlation is the 16e codebook that is almost the optimal for low correlation case. Since the adaptive codebook is even slightly better than the 16e codebook, the adaptive codebook has a performance very close to that of the optimal codebook. Further, the adaptive codebook has a performance very close to the ideal performance with infinite number of feedback bits, which is even better than that of the optimal codebook, for high correlation case. For low correlation case, the DFT codebook has the poorest performance. However, for high correlation case, the DFT codebook outperforms the 802.16e codebook by about one dB. 
         [0053]    Some of the system level results are shown as  400  of  FIG. 4  for a high correlation case. 802.16e is depicted as  410 , DFT as  420  and transformed at  430 . The legend “MUZF” stands for multi-user zero-forcing scheme and “SUCL” stands for single-user closed-loop MIMO scheme. Same as the link level results, the proposed adaptive codebook has the best performance with throughputs gain over the competitor DFT codebook for both MIMO schemes 
         [0054]    While certain features of the invention have been illustrated and described herein, many modifications, substitutions, changes, and equivalents may occur to those skilled in the art. It is, therefore, to be understood that the appended claims are intended to cover all such modifications and changes as fall within the true spirit of the invention.