Sparse channel estimation for MIMO OFDM systems
View Patent ↗A method for sparse channel estimation in MIMO OFDM systems with a plurality of subchannels having the same sparsity structure is presented. The inventive method comprises initializing a plurality of residual vectors and observation generating matrices modeling the channel, sending a pilot signal for each subcarrier, converting the pilot signals to tap positions, detecting an optimal tap position, updating the residual vectors by removing the one residual vector having the optimal tap position, updating the generating matrices in accordance with the optimal residual vector, calculating weighted residuals based on the updated residual vectors, and repeating the steps, except initializing, until a stopping condition is met, wherein the updated observation matrices estimate the sparse channel. In one embodiment, the observation generating matrices are omitted. In one embodiment, multiple vectors are removed during one iteration. Pilot placement and pilot allocation techniques are presented to optimize the method.
1. A method for sparse channel estimation in Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO OFDM) systems with a plurality of subchannels having same sparsity structure, said method comprising steps of: initializing a plurality of residual vectors modeling a plurality of subcarriers; initializing observation generating matrices modeling the channel; sending at least one pilot signal for each of a plurality of subcarriers; converting the pilot signals to tap positions in the residual vectors; detecting an optimal tap position in one of the residual vectors; updating the plurality of residual vectors by removing the one residual vector having the optimal tap position; updating the observation generating matrices in accordance with the residual vector having the optimal tap position; calculating, using a computer, weighted residuals based on the updated residual vectors; and repeating the steps of sending, converting, detecting, updating the residual vectors, updating the observation generating matrices, and calculating the weighted residuals, until a stopping condition is met, wherein the updated observation generating matrices estimate the sparse channel.
2. The method according to claim 1 , wherein all of the subchannels are treated jointly.
3. The method according to claim 1 , wherein the step of sending comprises randomly placing the pilot signals in each of the subcarriers.
4. The method according to claim 1 , wherein the step of sending comprises using an optimal approach to allocate the pilot signals to the subcarriers.
5. The method according to claim 1 , wherein the stopping condition is one of a maximum number of iterations, and the weighted residuals are less than a threshold.
6. A method for sparse channel estimation in Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO OFDM) systems with a plurality of subchannels having same sparsity structure, said method comprising steps of: initializing a plurality of residual vectors modeling a plurality of subcarriers; initializing observation generating matrices modeling the channel; sending at least one pilot signal for each of a plurality of subcarriers; converting the pilot signals to tap positions in the residual vectors; updating the plurality of residual vectors by removing all of the residual vectors exceeding a correlation threshold; updating the observation generating matrices in accordance with the residual vector having the optimal tap position; calculating, using a computer, weighted residuals based on the updated residual vectors; and repeating the steps of sending, converting, detecting, updating the residual vectors, updating the observation generating matrices, and calculating the weighted residuals, until a stopping condition is met, wherein the updated observation generating matrices estimate the sparse channel.
7. The method according to claim 6 , wherein all of the subchannels are treated jointly.
8. The method according to claim 6 , wherein the step of sending comprises randomly placing the pilot signals in each of the subcarriers.
9. The method according to claim 6 , wherein the step of sending comprises using an optimal approach to allocate the pilot signals to the subcarriers.
10. The method according to claim 6 , wherein the stopping condition is one of a maximum number of iterations, and the weighted residuals are less than a threshold.
11. A method for sparse channel estimation in Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO OFDM) systems with a plurality of subchannels having same sparsity structure, said method comprising steps of: initializing a plurality of residual vectors modeling a plurality of subcarriers; initializing an observation generating matrix modeling the channel; sending at least one pilot signal for each of a plurality of subcarriers; converting the pilot signals to tap positions in the residual vectors; detecting an optimal tap position in one of the residual vectors; updating the plurality of residual vectors by removing the one residual vector having the optimal tap position; updating the observation generating matrix in accordance with the residual vector having the optimal tap position; calculating, using a computer, weighted residuals based on the updated residual vectors; and repeating the steps of sending, converting, detecting, updating the residual vectors, and calculating the weighted residuals, until a stopping condition is met, wherein the updated observation generation matrix estimates the sparse channel.
12. The method according to claim 11 , wherein all of the subchannels are treated jointly.
13. The method according to claim 11 , wherein the step of sending comprises randomly placing the pilots in each of the subcarriers.
14. The method according to claim 11 , wherein the step of sending comprises using an optimal approach to allocate the pilots to the subcarriers.
15. The method according to claim 11 , wherein the stopping condition is one of a maximum
number of iterations, and the weighted residuals are less than a threshold.