Research Article
Reconsider the Sparsity-Induced Least Mean Square Algorithms on Channel Estimation
@INPROCEEDINGS{10.1007/978-3-319-90802-1_8, author={Jie Wang and Shangang Fan and Jie Yang and Jian Xiong and Guan Gui}, title={Reconsider the Sparsity-Induced Least Mean Square Algorithms on Channel Estimation}, proceedings={Wireless Internet. 10th International Conference, WiCON 2017, Tianjin, China, December 16-17, 2017, Proceedings}, proceedings_a={WICON}, year={2018}, month={5}, keywords={Gradient decent Least mean square (LMS) algorithm Sparse penalty Sparse channel estimation Gaussian noises model}, doi={10.1007/978-3-319-90802-1_8} }- Jie Wang
Shangang Fan
Jie Yang
Jian Xiong
Guan Gui
Year: 2018
Reconsider the Sparsity-Induced Least Mean Square Algorithms on Channel Estimation
WICON
Springer
DOI: 10.1007/978-3-319-90802-1_8
Abstract
This paper surveys recent advances related to sparse least mean square (LMS) algorithms. Since standard LMS algorithm does not take advantage of the sparsity information about the channel being estimated, various sparse LMS algorithms that are aim at outperforming standard LMS in sparse channel estimation are discussed. Sparse LMS algorithms force the solution to be sparse by introducing a sparse penalty to the standard LMS cost function. Under the reasonable conditions on the training datas and parameters, sparse LMS algorithms are shown to be mean square stable, and their mean square error performance and convergence rate are better than standard LMS algorithm. We introduce the sparse algorithms under Gaussian noises model. The simulation results presented in this work are useful in comparing sparse LMS algorithms against each other, and in comparing sparse LMS algorithms against standard LMS algorithm.
