Research Article
Performance Analysis of Sparsity-Penalized LMS Algorithms in Channel Estimation
@INPROCEEDINGS{10.1007/978-3-319-73317-3_47, author={Jie Yang and Hao Huang and Jie Wang and Sheng Hong and Zijian Hua and Jian Zhang and Guan Gui}, title={Performance Analysis of Sparsity-Penalized LMS Algorithms in Channel Estimation}, proceedings={Advanced Hybrid Information Processing. First International Conference, ADHIP 2017, Harbin, China, July 17--18, 2017, Proceedings}, proceedings_a={ADHIP}, year={2018}, month={2}, keywords={Gradient descent Least mean squares Sparse constraint Adaptive channel estimation Compressive sensing}, doi={10.1007/978-3-319-73317-3_47} }- Jie Yang
Hao Huang
Jie Wang
Sheng Hong
Zijian Hua
Jian Zhang
Guan Gui
Year: 2018
Performance Analysis of Sparsity-Penalized LMS Algorithms in Channel Estimation
ADHIP
Springer
DOI: 10.1007/978-3-319-73317-3_47
Abstract
Least mean squares (LMS) algorithm was considered as one of the effective methods in adaptive system identifications. Different from many unknown systems, LMS algorithm cannot exploit any structure characteristics. In case of sparse channels, sparse LMS algorithms are proposed to exploit channel sparsity and thus these methods can achieve better estimation performance than standard one, under the assumption of Gaussian noise environment. Specifically, several sparse constraint functions, -norm, reweighted -norm and -norm, are developed to take advantage of channel sparsity. By using different sparse functions, these proposed methods are termed as zero-attracting LMS (ZA-LMS), reweighted ZA-LMS (RZA-LMS), reweighted -norm LMS (RL1-LMS) and -norm LMS (LP-LMS). Our simulation results confirm the priority of the new algorithm and show that the proposed sparse algorithms are superior to the standard LMS in number scenarios.