Zero‐attracting variable‐step‐size least mean square algorithms for adaptive sparse channel estimation |
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| Authors: | Yingsong Li Masanori Hamamura |
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| Affiliation: | Graduate School of Engineering, Kochi University of Technology, Kami‐shi, Japan |
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| Abstract: | Recently, sparsity‐aware least mean square (LMS) algorithms have been proposed to improve the performance of the standard LMS algorithm for various sparse signals, such as the well‐known zero‐attracting LMS (ZA‐LMS) algorithm and its reweighted ZA‐LMS (RZA‐LMS) algorithm. To utilize the sparsity of the channels in wireless communication and one of the inherent advantages of the RZA‐LMS algorithm, we propose an adaptive reweighted zero‐attracting sigmoid functioned variable‐step‐size LMS (ARZA‐SVSS‐LMS) algorithm by the use of variable‐step‐size techniques and parameter adjustment method. As a result, the proposed ARZA‐SVSS‐LMS algorithm can achieve faster convergence speed and better steady‐state performance, which are verified in a sparse channel and compared with those of other popular LMS algorithms. The simulation results show that the proposed ARZA‐SVSS‐LMS algorithm outperforms the standard LMS algorithm and the previously proposed sparsity‐aware algorithms for dealing with sparse signals. Copyright © 2015 John Wiley & Sons, Ltd. |
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| Keywords: | least mean square sparse channel estimation compressed sensing zero‐point attracting l1‐penalized least mean square variable‐step‐size adaptive filtering |
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