METHOD AND APPARATUS FOR MULTIPLE-INPUT MULTIPLE- OUTPUT FEEDBACK GENERATION

• US 20080187062A1
• Filed: 02/06/2008
• Published: 08/07/2008
• Est. Priority Date: 02/06/2007
• Status: Abandoned Application

• Current Assignee: Interdigital Technology Corporation (InterDigital, Inc.)
• Original Assignee: Interdigital Technology Corporation (InterDigital, Inc.)
• Inventors: Pan, Kyle Jung-Lin, Tsai, Allan Yingming
• Application Number:
  US12/027,148
• Publication Number:
  US 20080187062A1
• Time in Patent Office: Days
• US Class Current: 375/260
• CPC Class Codes: H04B 7/0417 Feedback systems
• CPC Class Codes: H04B 7/0478 Special codebook structures directed to feedback optimisation
• CPC Class Codes: H04B 7/063 Parameters other than those covered in groups H04B7/0623 - H04B7/0634, e.g. channel matrix rank or transmit mode selection
• CPC Class Codes: H04B 7/0641 Differential feedback
• CPC Class Codes: H04L 2025/03426 transmission using multiple-input and multiple-output channels
• CPC Class Codes: H04L 2025/03802 Signalling on the reverse channel
• CPC Class Codes: H04L 25/03343 Arrangements at the transmitter end

Abstract

Disclosed are a method and apparatus for generating feedback in multiple-input/multiple-output (MIMO) communications. Feedback is used to update a precoding matrix

Claims

1. A method of generating feedback in multiple-input/multiple-output (MIMO) communications, comprising:

receiving a first signal transmitted using a precoding matrix;

determining a value of a metric associated with the first signal;

calculating a metric function from the value of a metric;

determining an update to the precoding matrix which optimizes the metric, the update being calculated from the metric function;

updating the precoding matrix using the update; and

receiving a second signal transmitted using the updated precoding matrix.

2. The method of claim 1, further comprising updating the precoding matrix for more than one frequency band.

3. The method of claim 1, wherein optimizing the metric comprises at least one of:

maximizing a received power;

maximizing a signal-to-noise-ratio;

maximizing a signal-to-interference-ratio;

maximizing a signal-to-noise-and-interference ratio (SINR);

maximizing a channel capacity;

maximizing a reception rate;

minimizing a received interference level;

minimizing a mean square error (MSE); and

minimizing a bit error rate (BER).

4. The method of claim 1, wherein determining an update comprises calculating an update q[n] according to the equation [n]=M(H[n]S₁[n])−

M(H[n]S₀[n]), where M( ) is the metric function, H is a channel matrix and S₁ and S₀ are matrices representing signal flows.

5. The method of claim 1, wherein determining an update comprises determining a single bit.

6. The method of claim 1, wherein determining an update comprises determining more than one bit.

7. The method of claim 5, comprising calculating the single bit using the equation

s[n]=sign{∥

H[n+1]T⁺[n+1]∥

_F²−

∥

H[n+1]T⁻

[n+1]∥

_F²}

where H[n+1] is a channel matrix, T⁺[n+1] and T⁻

[n+1] are two possible updated precoding matrices, and ∥

∥

_F indicates a Frobenius norm.

8. The method of claim 7, comprising calculating the updated precoding matrices from the equations

T⁺[n+1]=T[n]+v∥

T[n]∥

U

and

T⁻

[n+1]=T[n]−

v∥

T[n]∥

U

where T[n] is a precoding matrix not yet updated, U is a random perturbation matrix and v is an update step size.

9. The method of claim 1, wherein determining an update comprises generating at least one set of set of random matrices.

10. The method of claim 9, wherein a combination of updated precoding matrix and random matrix to be used is signaled with one or more bits.

11. The method of claim 1, wherein determining an update comprises:

defining current and updated precoding matrices as points in a Grassmann manifold; and

determining a signal flow in the manifold between the points.

12. The method of claim 11, comprising computing a feedback sign bit based on a direction that optimizes the metric.

13. The method of claim 12, wherein computing the feedback sign bit b[n] comprises evaluating equation b[n]=sign (q[n]), where q[n] is an effective channel measurement for the direction that optimizes the metric.

14. The method of claim 13, comprising determining q[n] from the equation q[n]=M(H[n]S₁[n])−

M(H[n]S₀[n]), where

H[n] is a channel matrix;

M(•

) is a metric function;

S₁[n]=U[n−

1]exp(F[n])Y;

S₀[n]=U[n−

1]exp(−

F[n])Y;

U is a unitary matrix;

$Y=\left[\begin{array}{c}{I}_{N_s}\\ 0_{\left(N_t-N_s\right)\times <br><br>N_s}\end{array}\right];<br><br>$

I_Ns is an identity matrix;

0<sub>(Nt−

Ns)×

Ns</sub> is a matrix containing only zeros;

$F<br><br>\left[n\right]=\left[\begin{array}{cc}0& -G^H<br><br>\left[n\right]\\ G<br><br>\left[n\right]& 0\end{array}\right];<br><br><br><br>\mathrm{and}$

G is a random matrix.

15. The method of claim 14, wherein the metric function is calculated using one of the equations

$M=\left(\tilde{H}\right)=-\mathrm{trace}({({\tilde{H}}^H}^{}$

H ~ + 1 SNR 

I ) - 1 ) $\mathrm{and}$ $M<br><br>\left(\tilde{H}\right)={\log }_2<br><br>\det <br><br>\left({\tilde{H}}^H<br><br>\tilde{H}+\frac{1}{\mathrm{\rho <br><br>}}<br><br>I\right).$

16. The method of claim 14, comprising generating the random matrix using independent and identical distribution (i.i.d.) complex Gaussian distributions of zero mean and a variance.

17. The method of claim 14, comprising generating the random matrix using a uniform distribution of random number between −

1 and 1.

18. The method of claim 17, comprising:

normalizing the random numbers to have norm equal to 1; and

scaling the normalized numbers by a scalar.

19. The method of claim 18, wherein the scalar is static.

20. The method of claim 18, comprising adjusting the scalar dynamically.

21. The method of claim 14, comprising adjusting the random matrix based on at least one of:

a speed; and

a Doppler shift.

22. The method of claim 9 comprising synchronously generating the at least one set of random matrices at a transmitter and at a receiver.

23. The method of claim 9 comprising receiving the at least one set of random matrices multiplexed with data.

24. The method of claim 9 comprising preconfiguring the at least one set of random matrices and storing the at least one set of matrices in memory in a transmitter and in a receiver.

25. The method of claim 1, wherein updating the precoding matrix T[n] comprises:

creating a matrix U[n] by concatenating the precoding matrix and a matrix which contains columns as an orthonormal basis of an orthogonal complement of the matrix, thereby making U[n] unitary; and

calculating an updated precoding matrix T[n+1] using the equation T[n+1]=U[n]exp(b[n+1]F[n+1])Y, where

b[n+1] is a feedback sign bit;

$Y=\left[\begin{array}{c}{I}_{N_s}\\ 0_{\left(N_t-N_s\right)\times <br><br>N_s}\end{array}\right];<br><br>$

I_Ns is an identity matrix;

0<sub>(Nt−

Ns)×

Ns</sub> is a matrix containing only zeros;

$F<br><br>\left[n\right]=\left[\begin{array}{cc}0& -G^H<br><br>\left[n\right]\\ G<br><br>\left[n\right]& 0\end{array}\right];<br><br><br><br>\mathrm{and}$

G is a random matrix.

26. The method of claim 1, wherein updating the precoding matrix T[n] comprises:

decomposing a known random matrix G according to the equation G[n+1]=V₂Θ

V₁^H, where Θ

is a diagonal matrix comprising Ns elements θ

₁, θ

₂, . . . , θ

_Ns, N_s being the dimensionality of a subspace of the channel matrix;

defining diagonal matrix C as C=diag(cos θ

₁, cos θ

₂, . . . , cos θ

_Ns);

defining diagonal matrix S as S=diag(sin θ

₁, sin θ

₂, . . . , sin θ

_Ns);

creating a unitary matrix U[n] by concatenating the precoding matrix and a matrix which contains columns as an orthonormal basis of an orthogonal complement of the precoding matrix; and

calculating an updated precoding matrix T[n+1] using the equation

$T<br><br>\left[n+1\right]=U<br><br>\left[n\right]<br><br>\left[\begin{array}{c}{V}_1<br><br>C\\ V_2<br><br>S\end{array}\right].$

58. The method of claim 25, wherein determining an update comprises generating at least one set of random matrices.

59. The method of claim 58 wherein a combination of an updated precoding matrix and a random matrix to be used is signaled with one or more bits.

60. The method of claim 4 wherein n indicates a time interval, a frequency interval, a subcarrier, a frequency band, a resource block, or a resource block group in any combination.

61. The method of claim 14 wherein n indicates a time interval, a frequency interval, a subcarrier, a frequency band, a resource block, or a resource block group in any combination.

62. The method of claim 25 wherein n indicates a time interval, a frequency interval, a subcarrier, a frequency band, a resource block, or a resource block group in any combination.

27. A wireless transmit/receive unit (WTRU) configured for providing feedback in multiple-input/multiple-output (MIMO) communications comprising:

a transmitter;

a receiver; and

a processor;

the receiver configured to

receive a first signal transmitted using a precoding matrix, and

receive a second signal transmitted using an updated precoding matrix;

the processor configured to;

determine a value of a metric associated with the first signal,

calculate a metric function from the value of the metric;

determine, using the metric function, an update to the precoding matrix which optimizes the metric;

the transmitter configured to transmit the optimizing update.

28. The WTRU of claim 27, wherein the processor is configured to optimize the metric by performing at least one of:

maximizing a received power;

maximizing a signal-to-noise-ratio;

maximizing a signal-to-interference-ratio;

maximizing a signal-to-noise-and-interference ratio (SINR);

maximizing a channel capacity;

maximizing a reception rate;

minimizing a received interference level;

minimizing a mean square error (MSE); and

minimizing a bit error rate (BER).

29. The WTRU of claim 27, wherein the transmitter is configured to determine the update by calculating an update q[n] according to the equation q[n]=M(H[n]S₁[n])−

M(H[n]S₀[n]), where MO is the metric function, n represents a time instance, H is a channel matrix and S₁ and S₀ are matrices representing signal flows.

30. The WTRU of claim 27, wherein the processor is configured for determining the update by determining a single bit.

31. The WTRU of claim 30, wherein the processor is configured to calculate the single bit using the equation

s[n]=sign{∥

H[n+1]T⁺[n+1]∥

_F²−

∥

H[n+1]T⁻

[n+1]∥

_F²}

where H[n+1] is a channel matrix, T⁺[n+1] and T⁻

[n+1] are two possible updated precoding matrices; and

∥

∥

_F indicates a Frobenius norm.

32. The WTRU of claim 31, wherein the processor is configured to calculate the updated precoding matrices T⁺[n+1] and T⁻

[n+1] from the equations

T⁺[n+1]=T[n]+v∥

T[n]∥

U

and

T⁻

[n+1]=T[n]−

v∥

T[n]∥

U

where T[n] is a precoding matrix not yet updated, U is a random perturbation matrix and v is an update step size.

33. The WTRU of claim 27, wherein the processor is configured to determine the update using a random matrix.

34. The WTRU of claim 27, wherein the processor is configured to determine the update by determining a signal flow between points representing current and updated precoding matrices in a Grassmann manifold

35. The WTRU of claim 34 wherein the processor is configured to compute a feedback sign bit based on a direction that optimizes the metric.

36. The WTRU of claim 35 wherein the processor is configured to compute the feedback sign bit b[n] by evaluating the equation b[n]=sign (q[n]), where q[n] is an effective channel measurement for the direction that optimizes the metric.

37. The WTRU of claim 36 wherein the processor is configured to determine q[n] from the equation q[n]=M(H[n]S₁[n])−

M(H[n]S₀[n]), where

H[n] is the channel matrix at time instance n;

M is a metric function;

S₁[n]=U[n−

1]exp(F[n])Y;

S₀[n]=U[n−

1]exp(−

F[n])Y;

U is a unitary matrix;

$Y=\left[\begin{array}{c}{I}_{N_s}\\ 0_{\left(N_t-N_s\right)\times <br><br>N_s}\end{array}\right];<br><br>$

I_Ns is an identity matrix;

0<sub>(Nt−

Ns)×

Ns</sub> is a matrix containing only zeros;

$F<br><br>\left[n\right]=\left[\begin{array}{cc}0& -G^H<br><br>\left[n\right]\\ G<br><br>\left[n\right]& 0\end{array}\right];<br><br><br><br>\mathrm{and}$

G is a random matrix.

38. The WTRU of claim 37, wherein the processor is configured to calculate the metric function using one of the equations

$M=\left(\tilde{H}\right)=-\mathrm{trace}$



( ( H ~ H 

H ~ + 1 SNR 

I ) - 1 ) $\mathrm{and}$ $M<br><br>\left(\tilde{H}\right)={\log }_2<br><br>\det <br><br>\left({\tilde{H}}^H<br><br>\tilde{H}+\frac{1}{\mathrm{\rho <br><br>}}<br><br>I\right).$

39. The WTRU of claim 37, wherein the processor is configured to determine q[n] when the random matrix is generated with independent and identical distribution (i.i.d.) complex Gaussian distributions of zero mean and a variance.

40. The WTRU of claim 37, wherein the processor is configured to determine q[n] when the random matrix is generated using a uniform distribution of random numbers between −

1 and 1.

41. The WTRU of claim 33 wherein the receiver is configured to receive the random matrix multiplexed with data and the processor is configured to determine q[n] from the random matrix when so received.

42. The WTRU of claim 33 comprising circuitry for generating the random matrix synchronously with another generator.

43. The WTRU of claim 33 comprising a memory configured for storing a set of preconfigured random matrices.

44. A processor for updating a precoding matrix, comprising:

a unitary module configured to generate a unitary matrix;

a randomizing module configured to generate a random matrix; and

a precoding module configured to receive the unitary matrix and random matrix and generate therefrom an updated precoding matrix.

45. The processor of claim 44, wherein the precoding module is configured to generate the updated precoding matrix using the equation T[n+1]=U[n]exp(b[n+1]F[n+1])Y where

b[n+1] is a feedback sign bit,

$Y=[\begin{array}{c}{I}_{N_s}\\ 0_{\left(N_t-N_s\right)\times }\end{array}$

N s ] ,

I_Ns an identity matrix;

0<sub>(Nt−

Ns)×

Ns</sub> a matrix containing only zeros;

$F<br><br>\left[n\right]=\left[\begin{array}{cc}0& -G^H<br><br>\left[n\right]\\ G<br><br>\left[n\right]& 0\end{array}\right];<br><br>$

U[n] is a unitary matrix at time interval n generated by the unitary module; and

G[n] is a random matrix.

46. The processor of claim 45, wherein the precoding module is configured to update the precoding matrix by

decomposing the random matrix G according to the equation G[n+1]=V₂Θ

V₁^H, where Θ

is a diagonal matrix comprising N_s elements θ

₁, θ

₂, . . . , θ

_Ns, N_s being N_s being the dimensionality of a subspace of the channel matrix;

generating diagonal matrix C as C=diag(cos θ

₁, cos θ

₂, . . . , cos θ

_Ns);

generating diagonal matrix S as S=diag(sin θ

₁, sin θ

₂, . . . , sin θ

_Ns); and

calculating an updated precoding matrix T[n+1] using the equation

$T<br><br>\left[n+1\right]=U<br><br>\left[n\right]<br><br>\left[\begin{array}{c}{V}_1<br><br>C\\ V_2<br><br>S\end{array}\right].$

47. The processor of claim 44, wherein the unitary module is configured to generate the unitary matrix by concatenating a current precoding matrix and a matrix which contains columns as an orthonormal basis of an orthogonal complement of the current precoding matrix.

48. The processor of claim 44, wherein the randomizing module is configured for generating the random matrix using independent and identical distribution (i.i.d.) complex Gaussian distributions of zero mean and a variance.

49. The processor of claim 44, wherein the randomizing module is configured for generating the random matrix using a uniform distribution of random numbers between −

1 and 1.

50. The processor of claim 49, wherein the randomizing module is configured for normalizing the random numbers to have norm equal to 1 and scaling the normalized numbers by a scalar.

51. The processor of claim 50, wherein the randomizing module is configured to use a static scalar.

52. The processor of claim 50 wherein the randomizing module is configured to use a scalar which is dynamically adjusted.

53. The processor of claim 52, wherein the scalar is dynamically adjusted according to a speed or a Doppler shift associated with a moving unit.

54. The processor of claim 44, wherein the randomizing module is configured for synchronously generating the random matrix with another generator.

55. The processor of claim 44 further comprising a multiplexer configured for multiplexing the random matrix with data.

56. The processor of claim 44, further comprising rank adaptation circuitry configured to receive rank adaptation information and convey the information to the precoding module, to be used in the updating of the precoding matrix.

57. The processor of claim 53 further comprising Doppler adjustment circuitry configured for receiving information on the speed or Doppler shift and conveying the information to the randomizing module, for use in generating the random matrix.