Reduced complexity sliding window based equalizer

• US 20050025267A1
• Filed: 06/24/2004
• Published: 02/03/2005
• Est. Priority Date: 03/03/2003
• Status: Active Grant

• Current Assignee: Interdigital Technology Corporation (InterDigital, Inc.)
• Original Assignee: Interdigital Technology Corporation (InterDigital, Inc.)
• Inventors: Yang, Rui, Reznik, Alexander, Zeira, Ariela, Li, Bin
• Granted Patent: US 7,042,967 B2
• Time in Patent Office: Days
• US Class Current: 375/346
• CPC Class Codes: H04B 1/71052 using decorrelation matrix
• CPC Class Codes: H04L 2025/03426 transmission using multiple-input and multiple-output channels
• CPC Class Codes: H04L 2025/03509 fractionally spaced H04L2025/03515 takes precedence
• CPC Class Codes: H04L 2025/03605 Block algorithms
• CPC Class Codes: H04L 25/0212 of impulse response
• CPC Class Codes: H04L 25/0242 using matrix methods
• CPC Class Codes: H04L 25/03057 with a recursive structure H04L25/03031 takes precedence
• CPC Class Codes: H04L 25/03292 with channel estimation circuitry

Abstract

The present invention has many aspects. One aspect of the invention is to perform equalization using a sliding window approach. A second aspect reuses information derived for each window for use by a subsequent window. A third aspect utilizes a discrete Fourier transform based approach for the equalization. A fourth aspect relates to handling oversampling of the received signals and channel responses. A fifth aspect relates to handling multiple reception antennas. A sixth embodiment relates to handling both oversampling and multiple reception antennas.

Claims

1. A method for data estimation is a wireless communications system, the method comprising:

producing a received vector;

processing the received vector using a sliding window based approach, where a plurality of windows are processed;

for each window of the plurality of windows;

transforming a non-Toeplitz channel response matrix into a Toeplitz matrix;

transforming the Toeplitz matrix into a circulant channel response matrix; and

using the circulant channel response matrix in a discrete Fourier transform based approach for estimating a data vector corresponding to that window; and

combining the data vector estimated in each window to form a combined data vector.

2. The method of claim 1 wherein the received vector is produced by sampling at a multiple of the chip rate.

3. The method of claim 2 wherein the received vector is processed by a root-raised cosine filter prior to the sliding window based processing.

4. The method of claim 3 wherein the sliding window based processing ignores a correlation between noise associated with each multiple of the chip rate samples.

5. The method of claim 3 wherein the sliding window based approach uses a received vector and a channel response matrix arranged in a natural order of estimated in the matrix and the arranged channel response matrix is a block-Toeplitz matrix, the natural order is an order that elements of the received vector and channel response matrix were actually received.

6. The method of claim 1 wherein a received data block processing is performed more frequently than channel filtering.

7. The method of claim 1 wherein the received vector comprises received samples from multiple receiving antennas at a multiple of the chip rate and the sliding window based approach is based on an assumption that noise is uncorrelated for each multiple of the chip rate samples and across the multiple antennas.

8. The method of claim 1 wherein the received vector comprises received samples from multiple receiving antennas at a multiple of the chip rate and the sliding window based approach is based on an assumption that noise is uncorrelated for each multiple of the chip rate samples and correlated across the multiple antennas.

9. The method of claim 1 wherein the received vector comprises received samples from multiple receiving antennas at a multiple of the chip rate and the sliding window based approach is based on an assumption that noise is correlated for each multiple of the chip rate samples and uncorrelated across the multiple antennas.

10. The method of claim 1 wherein the received vector comprises received samples from multiple receiving antennas at a multiple of the chip rate and the sliding window based approach is based on an assumption that noise is correlated for each multiple of the chip rate samples and correlated across the multiple antennas.

11. The method of claim 1 wherein a cross correlation of a noise vector is used in the sliding window processing and a pulse shaping filter is used to process received signals and a discrete Fourier transform of the pulse shaping filter is predetermined and multiplied by a measured noise variance to determine a discrete Fourier transform of the noise vector cross correlation.

12. The method of claim 1 wherein a cross correlation of a noise vector is used in the sliding window processing and a pulse shaping filter is used to process received signals and a discrete Fourier transform of an ideal pulse shape is multiplied by a measured noise variance to determine a discrete Fourier transform of the noise vector cross correlation.

13. A wireless transmit/receive unit (WTRU) comprising:

an input for receiving a received vector;

a channel equalizer for processing the received vector using a sliding window based approach, where a plurality of windows are processed;

for each window of the plurality of windows;

transforming a non-Toeplitz channel response matrix into a Toeplitz matrix;

transforming the Toeplitz matrix into a circulant channel response matrix; and

using the circulant channel response matrix in a discrete Fourier transform based approach for estimating a data vector corresponding to that window; and

combining the data vector estimated in each window to form a combined data vector.

14. The WTRU of claim 13 wherein the received vector is produced by sampling at a multiple of the chip rate.

15. The WTRU of claim 14 wherein the received vector is processed by a root-raised cosine filter prior to the sliding window based processing.

16. The WTRU of claim 15 wherein the sliding window based processing ignores a correlation between noise associated with each multiple of the chip rate samples.

17. The WTRU of claim 15 wherein the sliding window based approach uses a received vector and a channel response matrix arranged in a natural order of estimated in the matrix and the arranged channel response matrix is a block-Toeplitz matrix, the natural order is an order that elements of the received vector and channel response matrix were actually received.

18. The WTRU of claim 13 wherein a received data block processing is performed more frequently than channel filtering.

19. The WTRU of claim 13 wherein the received vector comprises received samples from multiple receiving antennas at a multiple of the chip rate and the sliding window based approach is based on an assumption that noise is uncorrelated for each multiple of the chip rate samples and across the multiple antennas.

20. The WTRU of claim 13 wherein the received vector comprises received samples from multiple receiving antennas at a multiple of the chip rate and the sliding window based approach is based on an assumption that noise is uncorrelated for each multiple of the chip rate samples and correlated across the multiple antennas.

21. The WTRU of claim 13 wherein the received vector comprises received samples from multiple receiving antennas at a multiple of the chip rate and the sliding window based approach is based on an assumption that noise is correlated for each multiple of the chip rate samples and uncorrelated across the multiple antennas.

22. The WTRU of claim 13 wherein the received vector comprises received samples from multiple receiving antennas at a multiple of the chip rate and the sliding window based approach is based on an assumption that noise is correlated for each multiple of the chip rate samples and correlated across the multiple antennas.

23. The WTRU of claim 13 wherein a cross correlation of a noise vector is used in the sliding window processing and a pulse shaping filter is used to process received signals and a discrete Fourier transform of the pulse shaping filter is predetermined and multiplied by a measured noise variance to determine a discrete Fourier transform of the noise vector cross correlation.

24. The WTRU of claim 13 wherein a cross correlation of a noise vector is used in the sliding window processing and a pulse shaping filter is used to process received signals and a discrete Fourier transform of an ideal pulse shape is multiplied by a measured noise variance to determine a discrete Fourier transform of the noise vector cross correlation.

25. A wireless transmit/receive unit (WTRU) comprising:

an input for receiving a received vector;

menas for processing the received vector using a sliding window based approach, where a plurality of windows are processed;

for each window of the plurality of windows;

means for transforming a non-Toeplitz channel response matrix into a Toeplitz matrix;

means for transforming the Toeplitz matrix into a circulant channel response matrix; and

means for using the circulant channel response matrix in a discrete Fourier transform based approach for estimating a data vector corresponding to that window; and

means for combining the data vector estimated in each window to form a combined data vector.

26. The WTRU of claim 25 wherein the received vector is produced by sampling at a multiple of the chip rate.

27. The WTRU of claim 26 wherein the received vector is processed by a root-raised cosine filter prior to the sliding window based processing.

28. The WTRU of claim 27 wherein the sliding window based processing ignores a correlation between noise associated with each multiple of the chip rate samples.

29. The WTRU of claim 27 wherein the sliding window based approach uses a received vector and a channel response matrix arranged in a natural order of estimated in the matrix and the arranged channel response matrix is a block-Toeplitz matrix, the natural order is an order that elements of the received vector and channel response matrix were actually received.

30. The WTRU of claim 25 wherein a received data block processing is performed more frequently than channel filtering.

31. The WTRU of claim 25 wherein the received vector comprises received samples from multiple receiving antennas at a multiple of the chip rate and the sliding window based approach is based on an assumption that noise is uncorrelated for each multiple of the chip rate samples and across the multiple antennas.

32. The WTRU of claim 25 wherein the received vector comprises received samples from multiple receiving antennas at a multiple of the chip rate and the sliding window based approach is based on an assumption that noise is uncorrelated for each multiple of the chip rate samples and correlated across the multiple antennas.

33. The WTRU of claim 25 wherein the received vector comprises received samples from multiple receiving antennas at a multiple of the chip rate and the sliding window based approach is based on an assumption that noise is correlated for each multiple of the chip rate samples and uncorrelated across the multiple antennas.

34. The WTRU of claim 25 wherein the received vector comprises received samples from multiple receiving antennas at a multiple of the chip rate and the sliding window based approach is based on an assumption that noise is correlated for each multiple of the chip rate samples and correlated across the multiple antennas.

35. The WTRU of claim 25 wherein a cross correlation of a noise vector is used in the sliding window processing and a pulse shaping filter is used to process received signals and a discrete Fourier transform of the pulse shaping filter is predetermined and multiplied by a measured noise variance to determine a discrete Fourier transform of the noise vector cross correlation.

36. The WTRU of claim 25 wherein a cross correlation of a noise vector is used in the sliding window processing and a pulse shaping filter is used to process received signals and a discrete Fourier transform of an ideal pulse shape is multiplied by a measured noise variance to determine a discrete Fourier transform of the noise vector cross correlation.

37. A base station comprising:

an input for receiving a received vector;

a channel equalizer for processing the received vector using a sliding window based approach, where a plurality of windows are processed;

for each window of the plurality of windows;

transforming a non-Toeplitz channel response matrix into a Toeplitz matrix;

transforming the Toeplitz matrix into a circulant channel response matrix; and

using the circulant channel response matrix in a discrete Fourier transform based approach for estimating a data vector corresponding to that window; and

combining the data vector estimated in each window to form a combined data vector.

38. The base station of claim 37 wherein the received vector is produced by sampling at a multiple of the chip rate.

39. The base station of claim 38 wherein the received vector is processed by a root-raised cosine filter prior to the sliding window based processing.

40. The base station of claim 39 wherein the sliding window based processing ignores a correlation between noise associated with each multiple of the chip rate samples.

41. The base station of claim 39 wherein the sliding window based approach uses a received vector and a channel response matrix arranged in a natural order of estimated in the matrix and the arranged channel response matrix is a block-Toeplitz matrix, the natural order is an order that elements of the received vector and channel response matrix were actually received.

42. The base station of claim 37 wherein a received data block processing is performed more frequently than channel filtering.

43. The base station of claim 37 wherein the received vector comprises received samples from multiple receiving antennas at a multiple of the chip rate and the sliding window based approach is based on an assumption that noise is uncorrelated for each multiple of the chip rate samples and across the multiple antennas.

44. The base station of claim 37 wherein the received vector comprises received samples from multiple receiving antennas at a multiple of the chip rate and the sliding window based approach is based on an assumption that noise is uncorrelated for each multiple of the chip rate samples and correlated across the multiple antennas.

45. The base station of claim 37 wherein the received vector comprises received samples from multiple receiving antennas at a multiple of the chip rate and the sliding window based approach is based on an assumption that noise is correlated for each multiple of the chip rate samples and uncorrelated across the multiple antennas.

46. The base station of claim 37 wherein the received vector comprises received samples from multiple receiving antennas at a multiple of the chip rate and the sliding window based approach is based on an assumption that noise is correlated for each multiple of the chip rate samples and correlated across the multiple antennas.

47. The base station of claim 37 wherein a cross correlation of a noise vector is used in the sliding window processing and a pulse shaping filter is used to process received signals and a discrete Fourier transform of the pulse shaping filter is predetermined and multiplied by a measured noise variance to determine a discrete Fourier transform of the noise vector cross correlation.

48. The base station of claim 37 wherein a cross correlation of a noise vector is used in the sliding window processing and a pulse shaping filter is used to process received signals and a discrete Fourier transform of an ideal pulse shape is multiplied by a measured noise variance to determine a discrete Fourier transform of the noise vector cross correlation.

49. A base station comprising:

an input for receiving a received vector;

menas for processing the received vector using a sliding window based approach, where a plurality of windows are processed;

for each window of the plurality of windows;

means for transforming a non-Toeplitz channel response matrix into a Toeplitz matrix;

means for transforming the Toeplitz matrix into a circulant channel response matrix; and

means for using the circulant channel response matrix in a discrete Fourier transform based approach for estimating a data vector corresponding to that window; and

means for combining the data vector estimated in each window to form a combined data vector.

50. The base station of claim 49 wherein the received vector is produced by sampling at a multiple of the chip rate.

51. The base station of claim 50 wherein the received vector is processed by a root-raised cosine filter prior to the sliding window based processing.

52. The base station of claim 51 wherein the sliding window based processing ignores a correlation between noise associated with each multiple of the chip rate samples.

53. The base station of claim 52 wherein the sliding window based approach uses a received vector and a channel response matrix arranged in a natural order of estimated in the matrix and the arranged channel response matrix is a block-Toeplitz matrix, the natural order is an order that elements of the received vector and channel response matrix were actually received.

54. The base station of claim 49 wherein a received data block processing is performed more frequently than channel filtering.

55. The base station of claim 49 wherein the received vector comprises received samples from multiple receiving antennas at a multiple of the chip rate and the sliding window based approach is based on an assumption that noise is uncorrelated for each multiple of the chip rate samples and across the multiple antennas.

56. The base station of claim 49 wherein the received vector comprises received samples from multiple receiving antennas at a multiple of the chip rate and the sliding window based approach is based on an assumption that noise is uncorrelated for each multiple of the chip rate samples and correlated across the multiple antennas.

57. The base station of claim 49 wherein the received vector comprises received samples from multiple receiving antennas at a multiple of the chip rate and the sliding window based approach is based on an assumption that noise is correlated for each multiple of the chip rate samples and uncorrelated across the multiple antennas.

58. The base station of claim 49 wherein the received vector comprises received samples from multiple receiving antennas at a multiple of the chip rate and the sliding window based approach is based on an assumption that noise is correlated for each multiple of the chip rate samples and correlated across the multiple antennas.

59. The base station of claim 49 wherein a cross correlation of a noise vector is used in the sliding window processing and a pulse shaping filter is used to process received signals and a discrete Fourier transform of the pulse shaping filter is predetermined and multiplied by a measured noise variance to determine a discrete Fourier transform of the noise vector cross correlation.

60. The base station of claim 49 wherein a cross correlation of a noise vector is used in the sliding window processing and a pulse shaping filter is used to process received signals and a discrete Fourier transform of an ideal pulse shape is multiplied by a measured noise variance to determine a discrete Fourier transform of the noise vector cross correlation.

61. An integrated circuit for estimating data from a received vector, the integrated circuit comprising:

a first input configured to receive a received vector;

a Fourier transform device for determining a Fourier transform of the received vector;

a second input configured to receive a channel response matrix;

a Toeplitz approximation device for determining a Toeplitz approximation for the channel response matrix;

a circulant approximation device for determining a circulant approximation for the Toeplitz approximation of the channel response matrix;

a Hermetian device for determining a Hermetian of the circulant approximation;

a cross correlation matrix determining device for determining a cross correlation matrix using the circulant approximation and the Hermetian of the circulant approximation;

a first diagonal determination device using a column of the Hermetian of the circulant approximation to produce a first diagonal matrix;

a second diagonal determination device using a column of the cross correlation matrix to produce a second diagonal matrix;

a multiplier for multiplying the first diagonal matrix, the second diagonal matrix and the Fourier transform of the received vector;

an inverse Fourier transform device for determining an inverse Fourier transform of a result of the multiplier to produce an estimate of the data vector.

62. A method for data estimation is a wireless communications system for a receiver having multiple receive antennas, the method comprising:

for each antenna, producing a received vector and a channel response matrix;

processing the received vector using a sliding window based approach, where a plurality of windows are processed;

for each window of the plurality of windows;

transforming each non-Toeplitz channel response matrix into a Toeplitz matrix;

transforming each Toeplitz matrix into a circulant channel response matrix; and

combining the circulant channel response matrices into a combined circulant channel response matrix;

using the combined circulant channel response matrix and a combined received vector comprising each received vector in a discrete Fourier transform based approach for estimating a data vector corresponding to that window; and

combining the data vector estimated in each window to form a combined data vector.

63. A method for data estimation is a wireless communications system for a receiver using multiple chip rate sampling, the method comprising:

for each multiple of the chip rate, producing a received vector and a channel response matrix;

processing the received vector using a sliding window based approach, where a plurality of windows are processed;

for each window of the plurality of windows;

transforming each non-Toeplitz channel response matrix into a Toeplitz matrix;

transforming each Toeplitz matrix into a circulant channel response matrix; and

combining the circulant channel response matrices into a combined circulant channel response matrix;

using the combined circulant channel response matrix and a combined received vector comprising each received vector in a discrete Fourier transform based approach for estimating a data vector corresponding to that window; and

combining the data vector estimated in each window to form a combined data vector.

64. The method of claim 63 wherein a receive-side root-raised cosine (RRC) filter is used and the sliding window based approach ignores a correlation of noise between samples of each multiple of the chip rate.

65. A method for data estimation is a wireless communications system for a receiver using multiple chip rate sampling and a receive-side root raised cosine filter, the method comprising:

for each multiple of the chip rate, producing a received vector and a channel response matrix;

processing the received vector using a sliding window based approach after receive-side root raised cosine filter processing, where a plurality of windows are processed;

for each window of the plurality of windows;

providing a combined received vector having elements of the received vectors in an order that each element was actually received;

providing a combined channel response matrix in a block Toeplitz structure having rows or columns of the channel response matrices in an order where same rows or columns of the matrices are grouped together in the combined channel response matrix;

transforming the combined channel response matrix into a block circulant combined channel response matrix; and

using the combined block circulant channel response matrix and the combined received vector comprising each received vector in a discrete Fourier transform based approach for estimating a data vector corresponding to that window; and

combining the data vector estimated in each window to form a combined data vector.

66. A method for data estimation is a wireless communications system for a receiver using multiple chip rate sampling and multiple receive antennas, the method comprising:

for each combination of a multiple of the chip rate and receive antenna, producing a received vector and a channel response matrix;

processing the received vector using a sliding window based approach, where a plurality of windows are processed;

for each window of the plurality of windows;

producing a combined circulant channel response matrix using the channel response matrices; and

using the combined circulant channel response matrix and a combined received vector comprising each received vector in a discrete Fourier transform based approach for estimating a data vector corresponding to that window; and

combining the data vector estimated in each window to form a combined data vector.

67. The method of claim 66 wherein the sliding window based approach uses an exact solution.

68. The method of claim 66 wherein the sliding window based approach assumes that noise between each receive antenna is uncorrelated.

69. The method of claim 66 wherein the sliding window based approach assumes that noise between multiples of the chip rate do not have a temporal correlation.

70. The method of claim 66 wherein the sliding window based approach assumes no correlation of noise between any of the combinations of the multiple of the chip rate and receive antenna exists.