Abstract:
The present invention discloses a base transceiver station (BTS) equipped with a plurality of antennas for improving the robustness of spatial division multiple access via nulling. The BTS comprises of a first matrix module receiving a plurality of signals from one or more customer premises equipments (CPEs) through the plurality of antennas and producing correspondingly a first plurality of covariance matrices representing the plurality of signals, a second matrix module receiving the first plurality of covariance matrices and generating correspondingly a set of derivative spatial signature matrices representing the CPEs respectively, a third matrix module receiving the derivative spatial signature matrices and producing correspondingly a second plurality of covariance matrices representing interferences of the CPEs, and an eigenvector module generating a plurality of beamforming vectors for the CPEs from the plurality of derivative spatial signature matrices and the second plurality of covariance matrices.

Description:
CROSS REFERENCE 
   This application is a continuation of U.S. patent application Ser. No. 11/695,575 filed Apr. 2, 2007, entitled “System and Method for Improving the Robustness of Spatial Division Multiple Access Via Nulling,” now issued as U.S. Pat. No. 7,450,673 on Nov. 11, 2008, which in turn claims priority under 35 U.S.C. §119(e) from U.S. Provisional Patent Application Ser. No. 60/836,716, filed Aug. 10, 2006, and entitled “SDMA with Robust Co-Channel Interference Nulling through Wide Beam-Width Multi-User Beamforming,” the entire contents of each of which are hereby incorporated by reference. 

   BACKGROUND 
   A communication channel in a wireless communication network can be shared by different wireless stations in the network. One example of channel sharing is that wireless stations, such as customer premises equipment (CPEs), transmit signals on the same frequency at different times or on different frequencies at the same time. 
   A wireless communication network that employs spatial division multiple access (SDMA) utilizes spatial diversity to increase the capacity of a network. In such a system, the CPEs sharing the same communication channel transmit signals on the same frequency at the same time. 
   In order to prevent the signals transmitted by the CPEs on the same frequency at the same time from interfering with one another, a base transceiver station (BTS) needs to isolate the signals in such a way that they will not reach unintended wireless stations. In other words, these CPEs must be able to reliably detect and retrieve the signals that are sent to them. 
   There are two common methods to provide isolation among the CPEs sharing the same communication channel in a wireless communication network that employs SDMA. They are polarization isolation and spatial isolation. Polarization isolation is a more technically challenging method, and yet, it only provides a limited degree of isolation among the CPEs. In an environment with severe multi-path, polarization isolation only provides a difference of 5 to 10 dB in gain between the signals and interference. 
   An antenna array system on a BTS in a wireless communication network provides a practical solution for spatial isolation. The BTS selects a set of CPEs to participate in SDMA such that the degree of isolation among them is greater than a predetermined threshold. Spatial isolation among CPEs is achieved by using beamforming and interference nulling for antenna arrays. 
   For example, in a system employing SDMA, the BTS determines the spatial signatures of CPEs A and B, which are identified as candidates for sharing a communication channel, and generates a different beamforming weighting vector for CPEs A and B by using their spatial signatures jointly. 
   When the BTS transmits a signal to CPE A, the beamforming weighting vector of CPE A is applied to the antenna array. The antenna beam pattern created with the beamforming weighting vector has a nulling angle positioned toward the direction of arrival (DOA) of the antennae beam pattern of CPE B, i.e., CPE A will not receive signals intended for CPE B. The same mechanism is also applied to CPE B. The method described above is called SDMA via nulling. 
   One issue related to an SDMA via nulling method is that the effectiveness of antenna nulling is very sensitive to the accuracy of the beamforming weighting vector generated from the spatial signatures. If the beamforming weighting vector is not accurate enough, employing an SDMA operation might not lead to an improvement in system capacity. It might even make the channel unusable for the CPEs sharing the same channel, which subsequently reduces the overall capacity of the wireless communication network. 
   For example, in order to support 16 QAM modulation in a wireless network employing SDMA, each CPE must have an SINR greater than 20 dB. Assume that CPEs A and B both have an SINR greater than 20 dB and both support 16 QAM modulation before sharing a communication channel. If the wireless communication network employing SDMA via nulling cannot provide an SINR greater than 20 dB for both CPEs A and B, employing SDMA will bring down the communication channel for both of them. 
   SDMA via nulling eliminates co-channel interference (CCI) by applying beamforming weighting vectors of the CPEs that are almost orthogonal to each other. The effectiveness of the elimination of the CCI by employing SDMA via nulling depends on the accuracy of the spatial signatures of a CPE. 
   However, in reality, the spatial signatures calculated from receiving signals are never ideal; therefore, it is not uncommon for a CCI leakage to occur in the wireless communication network employing SDMA via nulling. A CCI leakage produces a fixed noise level and puts a hard limit on the bit error rate (BER) of the wireless communication network. As such, what is desired is a system and method for providing a robust SDMA via nulling. 
   SUMMARY 
   The present invention discloses a base transceiver station (BTS) equipped with a plurality of antennas for improving the robustness of spatial division multiple access via nulling. The BTS comprises of a first matrix module receiving a plurality of signals from one or more customer premises equipments (CPEs) through the plurality of antennas and producing correspondingly a first plurality of covariance matrices representing the plurality of signals, a second matrix module receiving the first plurality of covariance matrices and generating correspondingly a set of derivative spatial signature matrices representing the CPEs respectively, a third matrix module receiving the derivative spatial signature matrices and producing correspondingly a second plurality of covariance matrices representing interferences of the CPEs, and an eigenvector module generating a plurality of beamforming vectors for the CPEs from the plurality of derivative spatial signature matrices and the second plurality of covariance matrices. 
   The construction and method of operation of the invention, however, together with additional objects and advantages thereof, will be best understood from the following description of specific embodiments when read in connection with the accompanying drawings. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
     The drawings accompanying and forming part of this specification are included to depict certain aspects of the invention. The invention may be better understood by reference to one or more of these drawings in combination with the description presented herein. It should be noted that the features illustrated in the drawings are not necessarily drawn to scale. 
       FIG. 1A  is a block diagram illustrating the first part of a system that calculates derivative spatial signature matrices for each CPE. 
       FIG. 1B  is a block diagram illustrating the second part of the system that generates beamforming weighting vectors for the CPEs sharing a communication channel. 
       FIGS. 2A and 2B  show two applications of the system and method disclosed in the present invention. 
       FIG. 3  is another application of the system and method disclosed in the present invention. 
   

   DESCRIPTION 
   The following detailed description of the invention refers to the accompanying drawings. The description includes exemplary embodiments, not excluding other embodiments, and changes may be made to the embodiments described without departing from the spirit and scope of the invention. The following detailed description does not limit the invention. Instead, the scope of the invention is defined by the appended claims. 
   The present invention discloses a system and method that improves the robustness of spatial division multiple access (SDMA) via nulling. The method disclosed in the present invention uses novel sets of the spatial signatures of customer premises equipments to generate beamforming weighting vectors for the CPEs to share a communication channel. 
   Rather than using the spatial signatures calculated from the receiving signals of a CPE to generate a beamforming weighting vector, the method disclosed in the present invention calculates derivative spatial signature matrices of a CPE and subsequently produces a covariance matrix of interference. A beamforming weighting vector is generated by using the derivative spatial signature matrices and the covariance matrix of interference of the CPEs sharing the same communication channel. 
   By applying a beamforming weighting vector generated by the aforementioned method to an antenna array on a base transceiver station, the antenna beam pattern of a CPE has a wider nulling angle positioned toward the direction of co-channel interference. The wider nulling angle makes an SDMA via nulling method more robust, because a small error in a covariance matrix of interference has less effect on the efficiency of the method. 
     FIGS. 1A and 1B  illustrate a system that generates beamforming weighting vectors for the CPEs sharing a communication channel in a wireless communication network employing SDAM via nulling.  FIG. 1A  is a block diagram illustrating the first part of the system that calculates derivative spatial signature matrices for each CPE.  FIG. 1B  is a block diagram illustrating the second part of the system that generates beamforming weighting vectors for the CPEs sharing the communication channel. 
     FIG. 1A  shows five modules: a receiver module  110 , a covariance matrix module  120 , a spatial signature module  130 , a derivative spatial signature matrix module  140 , and a memory module  150 . Assume that there are L CPEs sharing a communication channel. 
   The m antennas on a BTS receives a signal transmitted from CPE k at a receiving period i, and the BTS forms a vector of receiving signals 
               Y   i   k     =       [           y     i   ⁢           ⁢   1     k               y     i   ⁢           ⁢   2     k             ⋮             y   im   k           ]     ⁢   112       ,         
where kε{1, . . . , L) and y ij   k  is the receiving signals received by antenna j at a receiving period i, where jε{1, . . . , M). The vector  112  is stored in the memory module  150 . The receiver module  110  receives signals continuously and all the receiving vectors  112  are stored in the memory module  150 .
 
   The covariance module  120  takes a set of N k  receiving vectors  112  of CPE k from the memory module  150  and produces a covariance matrix of receiving signals  122  according to the following equation: 
               COV   k     =       1     N   k       ⁢       ∑     i   =   1       N   k       ⁢       [           y     i   ⁢           ⁢   1     k               y     i   ⁢           ⁢   2     k             ⋮             y   im   k           ]     ⁡     [           y     i   ⁢           ⁢   1       k   *             y     i   ⁢           ⁢   2       k   *           …         y   im     k   *             ]             ,         
where (y im   k )* is the conjugate-transpose of y im   k . The covariance matrix of receiving signals COV k    122  is stored in the memory module  150 . The covariance matrix module produces a covariance matrix of receiving signals continuously and all the covariance matrices  122  are stored in the memory module  150 .
 
   The spatial signature module  130  calculates a spatial signature  132  of CPE k by using the covariance matrix of receiving signals  122 . The spatial signatures  132  are stored in the memory module  150 . The spatial signature module calculates spatial signatures continuously and all spatial signatures are stored in the memory module  150 . 
   The derivative spatial signature matrix module  140  calculates a set of s k  derivative spatial signature matrices  142  of CPE k from a set of spatial signatures  132  calculated by the spatial signature module  130 . The set of derivative spatial signature matrices  142 , denoted as {R 1   k , . . . , R s     k     k }, is stored in the memory module  150 . 
   The BTS uses the system described in  FIG. 1A  to calculate a set of derivative spatial signature matrices of every CPE while the system described in  FIG. 1B  uses the derivative spatial signature matrices of all L CPEs to generate the beamforming weighting vectors of all L CPEs sharing a communication channel in a wireless communication network employing SDMA via nulling. 
     FIG. 1B  shows a beamforming weighting vector module  160 , which is composed of two modules: an interference covariance module  162  and an eigenvector module  166 . The beamforming weighting vector module  160  generates the beamforming weighting vector of CPE k by using the derivative spatial signature matrices of a set of L CPEs. 
   The interference covariance module  162  produces a covariance matrix of interference  164  of CPE k by using the derivative spatial signature matrices of all L CPEs, excluding CPE k, according to the following equation: 
               ∑       j   =   1     ,     j   ≠   k       L     ⁢           ⁢     (       1     s   j       ⁢       ∑     i   =   1       s   j       ⁢           ⁢       R   i   j     ⁢     R   i     j   H             )       ,         
where R i   j     H    is the conjugate-transpose of R i   j . Lines  154  and  156  depict two of the derivative spatial signature matrices of CPEs, excluding CPE k, while a line  152  depicts the derivative spatial signature matrix of CPE k. These derivative spatial signature matrices are retrieved from the memory module  150 .
 
   Based on the covariance matrix of interference  164  and the derivative spatial signature matrices  152 , the eigenvector module  166  generates a beamforming weighting vector W k    168  from the following eigenvalue equation: 
                   (         ∑       j   =   1     ,     j   ≠   k       L     ⁢           ⁢     (       1     s   j       ⁢       ∑     i   =   1       s   j       ⁢           ⁢       R   i   j     ⁢     R   i     j   H             )       +       σ   n   2     ⁢   I       )       -   1       ⁢     (       ∑     i   =   1       s   k       ⁢           ⁢       R   i   k     ⁢     R   i     k   H           )       )     .         
W k  is the eigenvector corresponding to the largest eigenvalue of the equation.
 
   The method to obtain beamforming weighting vectors in a wireless communication network employing the SDMA via nulling is applicable to other wireless communication networks that support multiple access, such as frequency division multiple access (FDMA), time division multiple access (TDMA), code division multiple access (CDMA), orthogonal frequency division multiplex multiple access (OFDM-MA) and any combinations of the above. In addition, frequency division duplex (FDD) and time division duplex (TDD) also allow multiple access in a wireless communication network. 
     FIGS. 2A and 2B  depict a system module  200  that calculates derivative spatial signature matrices of a CPE, an interference covariance module  230 , and an eigenvector module  240 . The system module  200  includes a receiver module  210  of a BTS in an OFDMA-based WiMax system, a covariance matrix module  220 , and a memory module  250 . 
     FIGS. 2A and 2B  show two applications of the system and method disclosed in the present invention in an OFDMA-based WiMax system with TDD employing SDMA via nulling. Assume CPEs A and B share one communication channel in a wireless communication network employing SDMA via nulling. In an OFDM system, a receiving signal is denoted as a unit of symbols. 
   In  FIG. 2A , the m antennas on the BTS receive OFDM symbols transmitted from CPE A at a receiving period i, and the BTS forms a vector of receiving signals  212 , denoted as 
               Y   i   A     =     [           y     i   ⁢           ⁢   1     A               y     i   ⁢           ⁢   2     A             ⋮             y   im   A           ]       ,         
where the receiving symbols received by an antenna k are shown as y ik   A , where kε{1, . . . , m). The vector  212  is stored in the memory module  250 . The receiver module  210  receives OFDM symbols from CPE A continuously and all receiving vectors  212  are stored in the memory module  250 .
 
   The same operation is also applied to CPE B. A vector of receiving OFDM symbols  214  at time j, is denoted as 
               Y   j   B     =     [           y     j   ⁢           ⁢   1     B               y   j2   B             ⋮             y   jm   B           ]       ,         
where the receiving symbols received by antenna j are shown as y jk   B , where kε{1, . . . , m). The receiver module  210  receives OFDM symbols from CPE B continuously and all receiving vectors  214  are stored in the memory module  250 .
 
   The covariance matrix module  220  takes a set of N A  receiving vectors  212  of CPE A from the memory module  250  and produces a covariance matrix of receiving signals of CPE A according to the following equation: 
               COV   A     =       1     N   A       ⁢       ∑     i   =   1       N   A       ⁢       [           y     i   ⁢           ⁢   1     A               y     i   ⁢           ⁢   2     A             ⋮             y   im   A           ]     ⁡     [           y     i   ⁢           ⁢   1       A   *             y     i   ⁢           ⁢   2       A   *           …         y   im     A   *             ]             ,         
where (y im   A )* is the conjugate-transpose of (y im   A ). The covariance matrix of receiving signals COV A    222  is stored in the memory module  250 . The covariance matrix module  220  produces a covariance matrix of receiving signals of CPE A continuously, and all the covariance matrices of receiving signals  222  are stored in the memory module  250 .
 
   The same operation is also applied to CPE B. A covariance matrix of receiving signals of CPE B is produced according to the following equation: 
               COV   B     =       1     N   B       ⁢       ∑     i   =   1       N   B       ⁢       [           y     i   ⁢           ⁢   1     B               y     i   ⁢           ⁢   2     B             ⋮             y   im   B           ]     ⁡     [           y     i   ⁢           ⁢   1       B   *             y     i   ⁢           ⁢   2       B   *           …         y   im     B   *             ]             ,         
where (y im   B )* is the conjugate-transpose of (y im   B ). COV B    224  is stored in the memory module  250 . The covariance matrix module  220  produces a covariance matrix of receiving signals of CPE B continuously, and all the covariance matrices of receiving signals  224  are stored in the memory module  250 . The memory module  250  has a set of m+1 covariance matrices of receiving signals  252  of CPE A, denoted as {COV 1   A , COV 2   A , . . . , COV m   A , COV A }, and a set of m+1 covariance matrices of receiving signals  254  of CPE B, denoted as {COV 1   B , COV 2   B , . . . , COV m   B , COV B }.
 
   Using the covariance matrices of receiving signals  254  of CPE B, the interference covariance module  230  produces a covariance matrix of interference  232  of CPE A according to the following equation: 
               ∑     i   =   1     m     ⁢           ⁢     Cov   i   B       +       Cov   B     .           
Similarly, the interference covariance module  230  uses the covariance matrices of receiving signals  252  of CPE A to produce a covariance matrix of interference  234  of CPE B according to the following equation:
 
   
     
       
         
           
             
               ∑ 
               
                 i 
                 = 
                 1 
               
               m 
             
             ⁢ 
             
                 
             
             ⁢ 
             
               Cov 
               i 
               A 
             
           
           + 
           
             
               Cov 
               A 
             
             . 
           
         
       
     
   
   Using the covariance matrix of interference  232  and the last covariance matrix of receiving signals COV A  in  252  of CPE A, the eigenvector module  240  generates a beamforming weighting vector  242  of CPE A, denoted as W A , using the following eigenvalue matrix: 
               [         1     m   +   1       ⁢     (         ∑     i   =   1     m     ⁢           ⁢     Cov   i   B       +     Cov   B       )       +       σ   n   2     ⁢   I       ]       -   1       ⁢       (     Cov   A     )     .           
W A  is the eigenvector corresponding to the largest eigenvalue of the matrix.
 
   Similarly, using the covariance matrix of interference  234  and the last covariance matrix of receiving signals COV B  in  254  of CPE B, the eigenvector module  240  generates a beamforming weighting vector  244  of CPE B, denoted as W B , using the following eigenvalue matrix: 
               [         1     m   +   1       ⁢     (         ∑     i   =   1     m     ⁢           ⁢     Cov   i   A       +     Cov   A       )       +       σ   n   2     ⁢   I       ]       -   1       ⁢       (     Cov   B     )     .           
W B  is the eigenvector corresponding to the largest eigenvalue of the matrix.
 
     FIGS. 2A and 2B  use the same system module  200 . The difference between  FIG. 2A  and  FIG. 2B  is that in  FIG. 2B  the eigenvector module  240  generates the beamforming weighting vectors of CPE A and B by using the covariance matrices of interference  232  and  234 . 
   A beamforming weighing vector  246  of CPE A, denoted as W A , is generated using the following eigenvalue matrix: 
               [         1     m   +   1       ⁢     (         ∑     i   =   1     m     ⁢           ⁢     Cov   i   B       +     Cov   B       )       +       σ   n   2     ⁢   I       ]       -   1       ⁢       (         ∑     i   =   1     m     ⁢           ⁢     Cov   i   A       +     Cov   A       )     .           
W A  is the eigenvector corresponding to the largest eigenvalue of the matrix. Similarly, a beamforming weighting vector  248  of CPE B, denoted as W B , is generated using the following eigenvalue matrix:
 
               [         1     m   +   1       ⁢     (         ∑     i   =   1     m     ⁢     Cov   i   B       +     Cov   B       )       +       σ   n   2     ⁢   I       ]       -   1       ⁢       (         ∑     i   =   1     m     ⁢           ⁢     Cov   i   B       +     Cov   B       )     .           
W B  is the eigenvector corresponding to the largest eigenvalue of the matrix.
 
     FIG. 3  is another application of the system and method disclosed in the present invention in an OFDMA-based WiMax system with TDD employing SDMA via nulling.  FIG. 3  depicts a system  300 , which is the same as the system  200  in  FIG. 2A , a transformation matrix module  310 , a derivative spatial signature matrix module  320 , an interference covariance matrix module  330 , and an eigenvector module  340 . 
   The memory module  250  in the system  300  has a set of m+1 covariance matrices of receiving signals {COV 1   A , COV 2   A , . . . , COV m   A , COV A } for CPE A and a set of m+1 covariance matrices of receiving signals {COV 1   B , COV 2   B , . . . , COV m   B , COV B } for CPE B. 
   Using the m+1 covariance matrices of receiving signals of CPE A, the transformation matrix module  310  produces m transformation matrices  312  for CPE A, denoted as T A , based on the following equations: T i   A =COV i+1   A (COV i   A ) −1 , where iε{1, . . . , m−1), and T m   A =COV A (COV m   A ) −1 . If (COV m   A ) −1  does not exist, the m-th transformation matrix T m   A  is produced based on the following equation: T m   A =COV m+1   A (COV m   A     H    COV m   A ) −1 COV m   A     H   . The transformation matrices  312  are stored in the memory module  250 . 
   Similarly, the transformation matrix module  310  uses the M+1 covariance matrix of receiving signals of CPE B to produce m transformation matrices  314  for CPE B, denoted as T B , based on the following equations: T i   B =COV i+1   B (COV i   B ) −1 , where iε{1, . . . , m−1), and T m   B =COV B (COV m   B ) −1 . If (COV m   B ) −1  does not exist, the m-th transformation matrix T m   B  is produced based on the following equation: T m   B =COV m+1   B (COV m   B     H   COV m   B ) −1 COV m   B     H   . The transformation matrices  314  are stored in the memory module  250 . 
   The derivative spatial signature matrix module  320  calculates a set of n derivative spatial signature matrices  322  from the set of transformation matrices  312  of CPE A according to the following equation: R i   A =T i   A Cov A , where iε{1, . . . , n) and n≦m. The last matrix in the set of the covariance matrices of receiving signals is COV A . The set of derivative spatial signature matrices  322  is stored in the memory module  250 . 
   The derivative spatial signature matrix module  320  calculates a set of n derivative spatial signature matrices  324  from the set of transformation matrices  314  of CPE B according to the following equation: R i   B =T i   B Cov B , where iε{1, . . . , n) and n≦m. The last matrix in the set of the covariance matrices of receiving signals is COV B . The set of derivative spatial signature matrices  324  is stored in the memory module  250 . The number of derivative spatial signature matrices for each CPE is predetermined according to the requirements of the wireless communication network. 
   Using the derivative spatial signature matrices  324  of CPE B, the interference covariance matrix module  330  produces a covariance matrix of interference  332  of CPE A according to the following equation: 
             ∑     i   =   1     n     ⁢           ⁢       R   i   B     .           
Similarly, the interference covariance matrix module  330  uses the derivative spatial signature matrices  322  of CPE A to produce a covariance matrix of interference  334  of CPE B according to the following equation:
 
   
     
       
         
           
             ∑ 
             
               i 
               = 
               1 
             
             n 
           
           ⁢ 
           
               
           
           ⁢ 
           
             
               R 
               i 
               A 
             
             . 
           
         
       
     
   
   The eigenvector module  340  generates the beamforming weighting vectors of CPEs A and B by using the covariance matrices of interference  332  and  334 . A beamforming weighting vector  342  of CPE A, denoted as W A , is generated using the following eigenvalue matrix: 
               [         1   m     ⁢     (       ∑     i   =   1     m     ⁢           ⁢     R   i   B       )       +       σ   n   2     ⁢   I       ]       -   1       ⁢       (       ∑     i   =   1     m     ⁢           ⁢     R   i   A       )     .           
W A  is the eigenvector corresponding to the largest eigenvalue of the matrix. In the same fashion, a beamforming weighting vector  344 , of CPE B, denoted as W B , is generated using the following eigenvalue matrix:
 
               [         1   m     ⁢     (       ∑     i   =   1     m     ⁢           ⁢     R   i   A       )       +       σ   n   2     ⁢   I       ]       -   1       ⁢       (       ∑     i   =   1     m     ⁢           ⁢     R   i   B       )     .           
W B  is the eigenvector corresponding to the largest eigenvalue of the matrix.
 
   The method disclosed in the present invention can reduce the noise caused by a CCI leakage by a significant level and is superior to existing methods. The method disclosed in the present invention increases the robustness of SDMA via nulling by creating an antenna beam pattern that has a wider nulling angle positioned toward the DOA of CCI. 
   The above illustration provides many different embodiments or embodiments for implementing different features of the invention. Specific embodiments of components and processes are described to help clarify the invention. These are, of course, merely embodiments and are not intended to limit the invention from that described in the claims. 
   Although the invention is illustrated and described herein as embodied in one or more specific examples, it is nevertheless not intended to be limited to the details shown, since various modifications and structural changes may be made therein without departing from the spirit of the invention and within the scope and range of equivalents of the claims. Accordingly, it is appropriate that the appended claims be construed broadly and in a manner consistent with the scope of the invention, as set forth in the following claims.