Apparatus, method and computer program product providing iterative recursive least squares (RLS) algorithm for coded MIMO systems

• US 20070286312A1
• Filed: 05/31/2007
• Published: 12/13/2007
• Est. Priority Date: 06/01/2006
• Status: Abandoned Application

• Current Assignee: Nokia Corporation
• Original Assignee: Nokia Corporation
• Inventors: Kim, Kyeong
• Application Number:
  US11/809,705
• Publication Number:
  US 20070286312A1
• Time in Patent Office: Days
• US Class Current: 375/340
• CPC Class Codes: H04L 2025/03414 Multicarrier
• CPC Class Codes: H04L 2025/03426 transmission using multiple-input and multiple-output channels
• CPC Class Codes: H04L 25/0204 of multiple channels
• CPC Class Codes: H04L 25/022 of frequency response
• CPC Class Codes: H04L 25/024 channel estimation algorithms
• CPC Class Codes: H04L 25/03159 operating in the frequency domain H04L25/03165, H04L25/03178 take precedence
• CPC Class Codes: H04L 27/2657 Carrier synchronisation
• CPC Class Codes: H04L 27/2695 with channel estimation, e.g. determination of delay spread, derivative or peak tracking channel estimation H04L25/0202

Abstract

A method embodiment receives a symbol vector on a plurality of channels. For each of the channels, the channel and a normalized frequency offset of the channel is estimated. Also for each of the channels, a soft decision value of the symbol vector is determined. An iterative recursive least squares RLS algorithm is executed on each of the channels that approximates a covariance matrix of a composite noise vector of the received symbol vector until a minimum change to the estimate of the channel and the estimate of the normalized frequency offset is reached. Using the recursively estimated channel and normalized frequency offset across each of the channels, a jointly decoded decision on the symbol vector is output. Embodiments for devices and computer programs are also detailed.

Claims

1. A method comprising:

receiving a symbol vector on a plurality of channels;

for each of the channels, estimating the channel and a normalized frequency offset of the channel;

for each of the channels, determining a soft decision value of the symbol vector;

executing an iterative recursive least squares RLS algorithm on each of the channels that approximates a covariance matrix of a composite noise vector of the received symbol vector until a minimum change to the estimate of the channel and the estimate of the normalized frequency offset is reached; and

using the recursively estimated channel and normalized frequency offset across each of the channels, outputting a jointly decoded decision on the symbol vector.

2. The method of claim 1, wherein the soft decision value is determined using an a posteriori probability APP, and the iterative RLS algorithm approximates the covariance matrix by incorporating the APP based soft decision.

3. The method of claim 2, wherein the iterative RLS algorithm incorporates the APP based soft decision using a variance of a symbol of the received symbol vector.

4. The method of claim 2, wherein the iterative RLS algorithm approximates the covariance matrix by further incorporating a previous channel estimate.

5. The method of claim 1, wherein the iterative RLS algorithm is executed on each of the channels received at each or at least two receive antennas.

6. The method of claim 1, wherein outputting the jointly decoded decision on the symbol vector comprises:

correcting each of the channels for the normalized frequency offset that was finally determined for that channel;

multiplying the received symbol vector corrected for the finally determined normalized frequency offset; and

multiplying by a fast Fourier transform matrix to output a demodulated signal vector.

7. The method of claim 1, wherein executing the iterative RLS algorithm comprises linearizing the received symbol vector on each channel using a Jacobean matrix.

8. A program of machine-readable instructions, tangibly embodied on a computer readable memory and executable by a digital data processor, to perform actions directed toward outputting a decision on a received symbol vector, the actions comprising:

receiving a symbol vector on a plurality of channels;

for each of the channels, estimating the channel and a normalized frequency offset of the channel;

for each of the channels, determining a soft decision value of the symbol vector;

executing an iterative recursive least squares RLS algorithm on each of the channels that approximates a covariance matrix of a composite noise vector of the received symbol vector until a minimum change to the estimate of the channel and the estimate of the normalized frequency offset is reached; and

using the recursively estimated channel and normalized frequency offset across each of the channels, outputting a jointly decoded decision on the symbol vector.

9. The program of claim 8, wherein the soft decision value is determined using an a posteriori probability APP, and the iterative RLS algorithm approximates the covariance matrix by incorporating the APP based soft decision.

10. The program of claim 9, wherein the iterative RLS algorithm incorporates the APP based soft decision using a variance of a symbol of the received symbol vector.

11. The program of claim 9, wherein the iterative RLS algorithm approximates the covariance matrix by further incorporating a previous channel estimate.

12. The program of claim 8, wherein the iterative RLS algorithm is executed on each of the channels received at each of at least two receive antennas.

13. The program of claim 8, wherein outputting the jointly decoded decision on the symbol vector comprises:

correcting each of the channels for the normalized frequency offset that was finally determined for that channel;

multiplying the received symbol vector corrected for the finally determined normalized frequency offset; and

multiplying by a fast Fourier transform matrix to output a demodulated signal vector.

14. The program of claim 8, wherein executing the iterative RLS algorithm comprises linearizing the received symbol vector on each channel using a Jacobean matrix.

15. A device comprising:

at least one receive antenna coupled to a receiver and adapted to receive a symbol vector on a plurality of channels;

a processor coupled to a memory adapted, for each of the channels;

to estimate the channel and a normalized frequency offset of the channel,

to determine a soft decision value of the symbol vector,

to execute an iterative recursive least squares RLS algorithm on each of the channels that approximates a covariance matrix of a composite noise vector of the received symbol vector until a minimum change to the estimate of the channel and the estimate of the normalized frequency offset is reached; and

thereafter

to apply the recursively estimated channel and the normalized frequency offset across each of the channels in order to determine a jointly decoded decision on the symbol vector.

16. The device of claim 15, wherein the processor determines the soft decision value using an a posteriori probability APP, and incorporates the APP based soft decision to approximate the covariance matrix.

17. The device of claim 16, wherein the processor uses a variance of a symbol of the received symbol vector to incorporate the APP based soft decision.

18. The device of claim 16, wherein the processor approximates the covariance matrix by further incorporating a previous channel estimate.

19. The device of claim 15, wherein the at least one receive antenna comprises at least two receive antennas, and the processor executes the iterative RLS algorithm on each of the channels received at each of at the at least two receive antennas.

20. The device of claim 15, wherein the processor outputs the jointly decoded decision on the symbol vector by:

correcting each of the channels for the normalized frequency offset that was finally determined for that channel;

multiplying the received symbol vector corrected for the finally determined normalized frequency offset; and

multiplying by a fast Fourier transform matrix to output a demodulated signal vector.

21. The device of claim 15, wherein the processor is adapted to linearize the received symbol vector on each channel using a Jacobean matrix in the iterative RLS algorithm.

22. The device of claim 15 comprising a mobile station.

23. A device comprising:

means for receiving a symbol vector on a plurality of channels;

for each of the channels, means for estimating the channel and a normalized frequency offset of the channel;

for each of the channels, means for determining a soft decision value of the symbol vector;

means for executing an iterative recursive least squares RLS algorithm on each of the channels that approximates a covariance matrix of a composite noise vector of the received symbol vector until a minimum change to the estimate of the channel and the estimate of the normalized frequency offset is reached; and

means for outputting a jointly decoded decision on the symbol vector using the recursively estimated channel and normalized frequency offset across each of the channels.

24. The device of claim 23, wherein:

the means for receiving comprises at least one receive antenna coupled to a receiver;

the means for determining comprises, for each channel, a detector of a processor; and

the means for estimating and means for executing comprises a processor coupled to a memory for storing a program, and the means for outputting comprises a terminal pin of the processor.