Research Article
Sparsity Adaptive Matching Pursuit Algorithm for Channel Estimation in Non-sample-spaced Multipath Channels
@INPROCEEDINGS{10.4108/icst.chinacom.2014.256399, author={Baohao Chen and Qimei Cui and Fan Yang and He Liu and Yujing Shang}, title={Sparsity Adaptive Matching Pursuit Algorithm for Channel Estimation in Non-sample-spaced Multipath Channels}, proceedings={9th International Conference on Communications and Networking in China}, publisher={IEEE}, proceedings_a={CHINACOM}, year={2015}, month={1}, keywords={channel estimation compressed sensing non-sample-spaced multipath channels sparsity adaptive matching pursuit}, doi={10.4108/icst.chinacom.2014.256399} }- Baohao Chen
Qimei Cui
Fan Yang
He Liu
Yujing Shang
Year: 2015
Sparsity Adaptive Matching Pursuit Algorithm for Channel Estimation in Non-sample-spaced Multipath Channels
CHINACOM
IEEE
DOI: 10.4108/icst.chinacom.2014.256399
Abstract
For non-sample-spaced multipath channels, multipath energy leakage leads to an increase in the channel sparsity and detection difficulties. In this paper, we propose the sparsity adaptive matching pursuit (SAMP) algorithm for the estimation of non-sample-spaced multipath channels. Compared with other greedy algorithms, the most innovative feature of the SAMP algorithm is its capability of signal reconstruction without the prior information of sparsity. To further improve the reconstruction quality, a regularized backtracking step which can flexibly remove the inappropriate atoms is adapted to SAMP algorithm. Simulation results show that channel estimation based on the proposed SAMP algorithm outperforms other greedy algorithms in non-sample-spaced multipath channels.