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

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Wireless Internet (WiCON 2017)

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.

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Acknowledgement

National Natural Science Foundation of China Grants (No. 61401069, No. 61701258), Jiangsu Specially Appointed Professor Grant (RK002STP16001), Innovation and Entrepreneurship of Jiangsu High-level Talent Grant (CZ0010617002), Natural Science Foundation of Jiangsu Province Grant (No. BK20170906), Natural Science Foundation of Jiangsu Higher Education Institutions Grant (No. 17KJB510044), High-Level Talent Startup Grant of Nanjing University of Posts and Telecommunications (XK0010915026) and “1311 Talent Plan” of Nanjing University of Posts and Telecommunications.

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Correspondence to Jie Wang .

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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Wang, J., Fan, S., Yang, J., Xiong, J., Gui, G. (2018). Reconsider the Sparsity-Induced Least Mean Square Algorithms on Channel Estimation. In: Li, C., Mao, S. (eds) Wireless Internet. WiCON 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 230. Springer, Cham. https://doi.org/10.1007/978-3-319-90802-1_8

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  • DOI: https://doi.org/10.1007/978-3-319-90802-1_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-90801-4

  • Online ISBN: 978-3-319-90802-1

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