Wireless Internet. 10th International Conference, WiCON 2017, Tianjin, China, December 16-17, 2017, Proceedings

Research Article

Reconsider the Sparsity-Induced Least Mean Square Algorithms on Channel Estimation

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  • @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
Jie Wang1,*, Shangang Fan1,*, Jie Yang1,*, Jian Xiong1,*, Guan Gui1,*
  • 1: Nanjing University of Posts and Telecommunications
*Contact email: 13675171572@163.com, sponder@126.com, jyang@niupt.edu.cn, jxiong@niupt.edu.cn, guiguan@niupt.edu.cn

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.