Methods and apparatuses for channel estimation in wireless networks

1. A method for recovering transmitted data at a receiver in a wireless communications network, the method comprising:

obtaining, at a receiving processor, 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 combined channel model matrix including a first plurality of combined channel model parameters, each of the first plurality of combined channel model parameters being associated with a corresponding index from among a plurality of indices;

choosing a first set of indices from among the plurality of indices, the first set of indices including 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;

estimating a channel in a sparse domain based on the combined channel model parameters corresponding to the indices in the first set of indices, the estimated channel in the sparse domain having less than m non-zero components, wherein m is less than a number of subcarriers in the wireless communications network; and

recovering the transmitted data based on the estimated channel.

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

computing a sparse measurement matrix based on the combined channel model parameters corresponding to the indices in 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 combined channel model parameters corresponding to the indices in 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 combined channel model matrix.

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

generating a residual signal value based on 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 combined channel model parameters corresponding to the indices in 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. A method for recovering transmitted data at a receiver in a wireless communications network, the method comprising:

obtaining, at a receiving processor, 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 combined channel model matrix including 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 a channel in a sparse domain based on the combined channel model parameters in the first set of combined channel model parameters, the estimated channel in the sparse domain having less than m non-zero components, wherein m is less than a number of subcarriers in the wireless communications network; and

recovering transmitted data based on the estimated channel.

11. The method of claim 10 , wherein the estimating step further comprises:

computing a sparse measurement matrix based on the combined channel model parameters in the first set of combined channel model parameters; and

estimating the channel based on the sparse measurement matrix.

12. The method of claim 11 , 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.

13. The method of claim 10 , 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 combined channel model matrix.

14. A receiver to receive wireless communications in a wireless communications network, the receiver comprising:

a receiving processor 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;

a channel estimation processor configured to,

combine the channel model parameters to generate a combined channel model matrix including a first plurality of combined channel model parameters, each of the first plurality of combined channel model parameters being associated with a corresponding index from among a plurality of indices,

choose a first set of indices from among the plurality of indices, the first set of indices including 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 a channel in a sparse domain based on the combined channel model parameters corresponding to the indices in the first set of indices, the estimated channel in the sparse domain having less than m non-zero components, wherein m is less than a number of subcarriers in the wireless communications network; and

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

15. The receiver of claim 14 , wherein the channel estimation processor is further configured to,

compute a sparse measurement matrix based on the combined channel model parameters corresponding to the indices in the first set of indices, and

estimate the channel based on the sparse measurement matrix.

16. The receiver of claim 15 , wherein the sparse measurement matrix includes values selected from a channel measurement matrix, and the channel estimation processor 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.