METHODS AND APPARATUSES FOR CHANNEL ESTIMATION IN WIRELESS NETWORKS

• US 20140086232A1
• Filed: 09/24/2012
• Published: 03/27/2014
• Est. Priority Date: 09/24/2012
• Status: Active Grant

• Current Assignee: Alcatel-Lucent SA (Nokia Corporation)
• Original Assignee: Alcatel-Lucent USA, Inc. (Nokia Corporation)
• Inventors: LING, Jonathan, CHIZHIK, Dmitry
• Granted Patent: US 8,929,390 B2
• Time in Patent Office: Days
• US Class Current: 370/346
• CPC Class Codes: H04L 25/0204 of multiple channels
• CPC Class Codes: H04L 25/0228 with direct estimation from sounding signals
• CPC Class Codes: H04L 25/024 channel estimation algorithms

Abstract

In a method for estimating a channel at a receiver in a wireless communications network, the receiver obtains channel model parameters for a received signal based on raw channel estimates for the received signal, where each raw channel estimate corresponds to an antenna at the receiver. The receiver combines, for each of a plurality of contiguous subcarriers in the wireless communications network, the channel model parameters to generate a first plurality of combined channel model parameters. The receiver then chooses a first set of indices corresponding to combined channel model parameters from among the first plurality of combined channel model parameters having magnitudes passing a combined channel model parameter threshold. The receiver then estimates the channel in a sparse domain based on the first set of indices.

Claims

1. A method for estimating a channel at a receiver in a wireless communications network, the method comprising:

obtaining, at the receiver, channel model parameters for a received signal, based on raw channel estimates for the received signal, each raw channel estimate corresponding to an antenna at the receiver;

combining the channel model parameters to generate a first plurality of combined channel model parameters;

choosing a first set of indices corresponding to combined channel model parameters from among the first plurality of combined channel model parameters having magnitudes passing a combined channel model parameter threshold; and

estimating the channel in a sparse domain based on the first set of indices.

2. The method of claim 1, wherein the estimating step further comprises:

computing a sparse measurement matrix based on the first set of indices; and

estimating the channel based on the sparse measurement matrix.

3. The method of claim 2, wherein the sparse measurement matrix includes values selected from a channel measurement matrix, the computing step further including,

selecting, from the channel measurement matrix, values corresponding to indices in the first set of indices as values of the sparse measurement matrix.

4. The method of claim 1, wherein the estimating step further comprises:

generating a sparse measurement matrix based on the first set of indices;

calculating estimated signal parameters associated with the received signal based on the sparse measurement matrix; and

estimating the channel based on the estimated signal parameters.

5. The method of claim 1, wherein the channel model parameters are in the form of a channel model parameter matrix including magnitudes of raw channel model parameters for the received signal, and the combining step includes,

combining magnitudes of the raw channel model parameters to obtain the first plurality of combined channel model parameters.

6. The method of claim 1, wherein the estimating the channel in the sparse domain further comprises:

generating a residual signal value based on the estimated signal parameters;

calculating a channel estimation decision metric based on the residual signal value;

comparing the channel estimation decision metric with a channel estimation decision threshold; and

estimating the channel in the sparse domain based on the sparse measurement matrix if the channel estimation decision metric passes the channel estimation decision threshold.

7. The method of claim 1, wherein the estimating step comprises:

iteratively obtaining a sparse measurement matrix based on the first set of indices; and

estimating the channel in the sparse domain based on the iteratively obtained sparse measurement matrix.

8. The method of claim 1, wherein the choosing step comprises:

comparing each of the plurality of combined channel model parameters with the combined channel model parameter threshold; and

choosing the first set of indices based on the comparing step.

9. The method of claim 1, wherein the choosing step comprises:

filtering indices corresponding to the first plurality of combined channel model parameters based on the combined channel model parameter threshold; and

choosing the first set of indices based on the filtering step.

10. The method of claim 9, wherein the first set of indices includes indices corresponding to combined channel model parameters from among the first plurality of combined channel model parameters passing the combined channel model parameter threshold.

11. The method of claim 1, further comprising:

recovering transmitted data based on the estimated channel.

12. A method for estimating a channel at a receiver in a wireless communications network, the method comprising:

obtaining, at the receiver, channel model parameters for a received signal based on raw channel estimates for the received signal, each raw channel estimate corresponding to an antenna at the receiver;

combining the channel model parameters to generate a first plurality of combined channel model parameters;

choosing a first set of combined channel model parameters from among the first plurality of combined channel model parameters based on magnitudes of the first plurality of combined channel model parameters and a combined channel model parameter threshold; and

estimating the channel in a sparse domain based on indices corresponding to the combined channel model parameters in the first set of combined channel model parameters.

13. The method of claim 12, wherein the estimating step further comprises:

computing a sparse measurement matrix based on the indices; and

estimating the channel based on the sparse measurement matrix.

14. The method of claim 13, wherein the sparse measurement matrix includes values selected from a measurement matrix, the computing step further including,

selecting, from the measurement matrix, values corresponding to the indices as values of the sparse measurement matrix.

15. The method of claim 12, further comprising:

recovering transmitted data based on the estimated channel.

16. The method of claim 12, wherein the channel model parameters are in the form of a channel model parameter matrix including magnitudes of raw channel model parameters for the received signal, and the combining step includes,

combining magnitudes of the raw channel model parameters to obtain the first plurality of combined channel model parameters.

17. A receiver comprising:

a receiver processing module configured to obtain channel model parameters for a received signal based on raw channel estimates for the received signal, each raw channel estimate corresponding to an antenna at the receiver; and

a channel estimation module configured to,

combine the channel model parameters to generate a first plurality of combined channel model parameters,

choose a first set of indices corresponding to combined channel model parameters from among the first plurality of combined channel model parameters having magnitudes passing a combined channel model parameter threshold, and

estimate the channel in a sparse domain based on the first set of indices.

18. The receiver of claim 17, further comprising:

a demodulation module configured to recover transmitted data based on the estimated channel.

19. The receiver of claim 17, wherein the channel estimation module is further configured to,

compute a sparse measurement matrix based on the first set of indices, and

estimate the channel based on the sparse measurement matrix.

20. The receiver of claim 19, wherein the sparse measurement matrix includes values selected from a channel measurement matrix, and the channel estimation module is further configured to,

select, from the channel measurement matrix, values corresponding to indices in the first set of indices to compute the sparse measurement matrix.