System and method for improving the robustness of spatial division multiple access via nulling

• US 7,801,239 B2
• Filed: 09/10/2008
• Issued: 09/21/2010
• Est. Priority Date: 08/10/2006
• Status: Active Grant

• Current Assignee: Cisco Technology, Inc. (Cisco Systems, Inc.)
• Original Assignee: Cisco Technology, Inc. (Cisco Systems, Inc.)
• Inventors: Guo, Li, Jin, Hang
• Primary Examiner(s): Bocure; Tesfaldet
• Application Number:
  US12/207,546
• Publication Number:
  US 20090004988A1
• Time in Patent Office: 741 Days
• Field of Search: 375/347, 375/316, 375/377, 375/299, 375/358, 455/91, 455/101, 455/132
• US Class Current: 375/267
• CPC Class Codes: H01Q 3/2611 Means for null steering; Adaptive interference nulling
• CPC Class Codes: H04B 7/0617 for beam forming
• CPC Class Codes: H04B 7/0842 Weighted combining
• CPC Class Codes: H04B 7/086 using weights depending on external parameters, e.g. direction of arrival [DOA], predetermined weights or beamforming
• CPC Class Codes: H04B 7/0865 Independent weighting, i.e. weights based on own antenna reception parameters

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.

Claims

1. An apparatus comprising:

a plurality of antennas;

a receiver coupled to the plurality of antennas, wherein the receiver is configured to generate a plurality of receive signals associated with transmissions from a plurality of remote devices that are detected by the plurality of antennas;

at least one processor module that is configured to;

produce a plurality of first covariance matrices from the plurality of receive signals;

generate a plurality of derivative spatial signature matrices for the plurality of remote devices from the plurality of first covariance matrices;

generate a plurality of second covariance matrices from the derivative spatial signature matrices, wherein the plurality of second covariance matrices represent interference associated with the plurality of remote devices;

generate a plurality of beamforming vectors from the plurality of derivative spatial signature matrices and from the plurality of second covariance matrices;

apply the plurality of beamforming vectors to signals derived from the transmissions received from the plurality of remote devices that overlap in frequency and time in order to recover transmissions sent from respective ones of the plurality of remote devices using spatial division multiplexing techniques.

2. The apparatus of claim 1, wherein the at least one processor module is configured to produce one second covariance matrix for a particular remote device k by computing:

$\underset{j=1,j\ne <br><br>k}{\overset{L}{\sum <br><br>}}<br><br><br><br>\left(\frac{1}{s^j}<br><br>\underset{i=1}{\overset{s^j}{\sum <br><br>}}<br><br><br><br>R_i^j<br><br>R_i^{j^H}\right),$

, where R_i^j is the derivative spatial signature matrix for remote device j, R_i^jH is the conjugate transpose of matrix R_i^j, L represents a number of remote devices sharing a communication channel, and S^j is the of derivative spatial signature matrices for remote device j.

3. The apparatus of claim 2, wherein the at least one processor module is configured to generate a beamforming vector W^k for remote device k using the following eigenvalue matrix:

${\left(\underset{j=1,j\ne <br><br>k}{\overset{L}{\sum <br><br>}}<br><br><br><br>\left(\frac{1}{s^j}<br><br>\underset{i=1}{\overset{s^j}{\sum <br><br>}}<br><br><br><br>R_i^j<br><br>R_i^{j^H}\right)+{\mathrm{\sigma <br><br>}}_n^2<br><br>I\right)}^{-1}<br><br>\left(\underset{i=1}{\overset{s^k}{\sum <br><br>}}<br><br><br><br>R_i^k<br><br>R_i^{k^H}\right)),$

wherein the beamforming vector W^k is the eigenvector corresponding to the largest eigenvalue of the eigenvalue matrix.

4. An apparatus comprising:

a plurality of antennas;

a receiver coupled to the plurality of antennas, wherein the receiver is configured to generate a plurality of receive signals associated with transmissions from a plurality of remote devices that are detected by the plurality of antennas;

at least one processor module that is configured to;

produce a plurality of first covariance matrices from the plurality of receive signals;

generate a plurality of transformation matrices from the first plurality of covariance matrices;

generate a plurality of derivative spatial signature matrices for the plurality of remote devices from the plurality of first covariance matrices and from the plurality of transformation matrices; and

generate a plurality of beamforming vectors from the plurality of derivative spatial signature matrices;

apply the plurality of beamforming vectors to signals derived from the transmissions received from the plurality of remote devices that overlap in frequency and time in order to recover transmissions sent from respective ones of the plurality of remote devices using spatial division multiplexing techniques.

5. The apparatus of claim 4, wherein the at least one processor module is further configured to generate a plurality of transformation matrices for the remote devices from the first plurality of covariance matrices associated with received signals according to the following equations:

T_i=COV_i+1(COV_i)<sup>−

1</sup> and T_m=COV(COV_m)<sup>−

1</sup>, where iε

{1,L,m−

1);

T_i is the i-th element of the plurality of transformation matrices;

COV_i is the i-th element;

COV is the last element of the first plurality of covariance matrices.

6. The apparatus of claim 5, wherein the at least one processor module is configured to calculate the plurality of derivative spatial signature matrices according to the following equations:

V_i=T_iCov, where iε

{1, . . . , n) and n≦

m;

T_i is the i-th element of the set of transformation matrices; and

COV is the last element of the first plurality of covariance matrices.

7. A method comprising:

receiving at a plurality of antennas of a first device signals associated with transmissions from a plurality of remote devices, wherein the transmissions from respective ones of the plurality of remote devices overlap in frequency and time;

computing a plurality of first covariance matrices from the plurality of receive signals;

generating a plurality of derivative spatial signature matrices for the plurality of remote devices from the plurality of first covariance matrices;

generating a plurality of second covariance matrices from the derivative spatial signature matrices, wherein the plurality of second covariance matrices represent interference associated with the plurality of remote devices;

generating a plurality of beamforming vectors from the plurality of derivative spatial signature matrices and the plurality of second covariance matrices;

applying the plurality of beamforming weight vectors to signals derived from the transmissions received at the plurality of antennas in order to recover transmissions sent from respective ones of the plurality of remote devices.

8. The apparatus of claim 7, wherein the at least one processor module is configured to compute second covariance matrix for remote device k according to the following equation:

$\underset{j=1,j\ne <br><br>k}{\overset{L}{\sum <br><br>}}<br><br><br><br>\underset{i=1}{\overset{n}{\sum <br><br>}}<br><br><br><br>R_i^j,$

where iε

{1, . . . , n), R_i^j is the i-th element of the plurality of derivative spatial signature matrices of remote device j and L represents the number of remote devices sharing a communication channel.

9. The apparatus of claim 8, wherein the at least one processor module generates a beamforming vector W^k for remote device k using the following eigenvalue matrix:

${\left[\underset{j=1,j\ne <br><br>k}{\overset{L}{\sum <br><br>}}<br><br><br><br>\frac{1}{m}<br><br>\left(\underset{i=1}{\overset{m}{\sum <br><br>}}<br><br><br><br>R_i^j\right)+{\mathrm{\sigma <br><br>}}_n^2<br><br>I\right]}^{-1}<br><br>\left(\underset{i=1}{\overset{m}{\sum <br><br>}}<br><br><br><br>R_i^k\right).$

10. The method of claim 7, wherein producing the plurality of second covariance matrices comprises producing one second covariance matrix for a particular remote device k by computing

$\sum _{}^{}$

j = 1 , j ≠

k L ⁢

⁢

( 1 s j ⁢

∑

i = 1 s j ⁢

⁢

R i j ⁢

R i j H ) ,

where R_i^j is the derivative spatial signature matrix for remote device j, R_i^jH is the conjugate transpose of matrix R_i^j, L represents a number of remote devices sharing a communication channel, and S^j is the of derivative spatial signature matrices for remote device j.

11. The method of claim 10, wherein generating the plurality of beamforming vectors comprises generating a beamforming vector W^k for remote device k using the following eigenvalue matrix:

${\left(\underset{j=1,j\ne <br><br>k}{\overset{L}{\sum <br><br>}}<br><br><br><br>\left(\frac{1}{s^j}<br><br>\underset{i=1}{\overset{s^j}{\sum <br><br>}}<br><br><br><br>R_i^j<br><br>R_i^{j^H}\right)+{\mathrm{\sigma <br><br>}}_n^2<br><br>I\right)}^{-1}<br><br>\left(\underset{i=1}{\overset{s^k}{\sum <br><br>}}<br><br><br><br>R_i^k<br><br>R_i^{k^H}\right)),$

wherein the beamforming vector W^k is the eigenvector corresponding to the largest eigenvalue of the eigenvalue matrix.

12. The method of claim 7, wherein generating the plurality of second covariance matrices comprises computing second covariance matrices for remote device k according to the following equation:

$\underset{j=1,j\ne <br><br>k}{\overset{L}{\sum <br><br>}}<br><br><br><br>\underset{i=1}{\overset{n}{\sum <br><br>}}<br><br><br><br>R_i^j,$

where iε

{1, . . . , n), R_i^j is the i-th element of the plurality of derivative spatial signature matrices of remote device j and L represents the number of remote devices sharing a communication channel.

13. The method of claim 12, wherein generating the plurality of beamforming vectors comprises generating a beamforming vector W^k for remote device k using the following eigenvalue matrix:

${\left[\underset{j=1,j\ne <br><br>k}{\overset{L}{\sum <br><br>}}<br><br><br><br>\frac{1}{m}<br><br>\left(\underset{i=1}{\overset{m}{\sum <br><br>}}<br><br><br><br>R_i^j\right)+{\mathrm{\sigma <br><br>}}_n^2<br><br>I\right]}^{-1}<br><br>\left(\underset{i=1}{\overset{m}{\sum <br><br>}}<br><br><br><br>R_i^k\right).$

14. An apparatus comprising:

a plurality of antennas;

a receiver coupled to the plurality of antennas, wherein the receiver is configured to generate a plurality of receive signals associated with transmissions from at least first and second remote devices that are detected by the plurality of antennas;

at least one processor module that is configured to;

produce receive vectors representing symbols received over a period of time from the first and second remote devices across the plurality of antennas;

compute covariance matrices for the first and second remote devices from a set of receive of receive vectors produced from symbols received from the first and second remote devices, respectively;

generate a covariance matrix of interference for the first remote device based on the covariance matrices for the second remote device and a covariance matrix of interference for the second remote device based on the covariance matrices for the first remote device; and

compute a beamforming weight vector for the first remote device from the covariance matrix of interference for the first remote device and a most recent covariance matrix for the first remote device and a beamforming weight vector for the second remote device from the covariance matrix of interference for the second remote device and a most recent covariance matrix for the second remote device.

15. The apparatus of claim 14, wherein the at least one processing module is configured to compute and store a set of m+1 covariance matrices, denoted {COV₁^A, COV₂^A, . . . , COV_m^A, COV^A} from symbols received from the first remote device over time and a set of m+1 covariance matrices {COV_1B, COV₂^B, . . . , COV_m^B, COV^B} from symbols received from the second remote device over time.

16. The apparatus of claim 15, wherein the at least one processing module is configured to compute the covariance matrix of interference for the first remote device according to the computation

$\sum ^{}_{}$

m i = 1 ⁢

Cov i B + Cov B

and to compute the covariance matrix of interference for the second remote device according to the computation

$\underset{i=1}{\overset{m}{\sum <br><br>}}<br><br><br><br>{\mathrm{Cov}}_i^A+{\mathrm{Cov}}^A.$

17. The apparatus of claim 16, wherein the at least one processing module is configured to compute a beamforming weight vector for the first remote device by computing an eigenvector corresponding to an eigenvalue of

${[\frac{1}{m+1}}^{}$

( ∑

m i = 1 ⁢

Cov i B + Cov B ) + σ

n 2 ⁢

I ] - 1 ⁢

( Cov A )

and to compute a beamforming weight vector for the second remote device by computing an eigenvector corresponding to an eigenvalue of

${\left[\frac{1}{m+1}<br><br>\left(\underset{i=1}{\overset{m}{\sum <br><br>}}<br><br><br><br>{\mathrm{Cov}}_i^A+{\mathrm{Cov}}^A\right)+{\mathrm{\sigma <br><br>}}_n^2<br><br>I\right]}^{-1}<br><br>\left({\mathrm{Cov}}^B\right).$

18. The apparatus of claim 16, wherein the at least one processing module is configured to compute a beamforming weight vector for the first remote device by computing an eigenvector corresponding to an eigenvalue of

${[\frac{1}{m+1}}^{}$

( ∑

m i = 1 ⁢

Cov i B + Cov B ) + σ

n 2 ⁢

I ] - 1 ⁢

( ∑

m i = 1 ⁢

Cov i A + Cov A )

and to compute a beamforming weight vector for the second remote device by computing an eigenvector corresponding to an eigenvalue of

${\left[\frac{1}{m+1}<br><br>\left(\underset{i=1}{\overset{m}{\sum <br><br>}}<br><br>{\mathrm{Cov}}_i^B+{\mathrm{Cov}}^B\right)+{\mathrm{\sigma <br><br>}}_n^2<br><br>I\right]}^{-1}<br><br>\left(\underset{i=1}{\overset{m}{\sum <br><br>}}<br><br><br><br>{\mathrm{Cov}}_i^B+{\mathrm{Cov}}^B\right).$