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A Variable Step-Size Strategy Based on Error Function for Sparse System Identification

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Abstract

The well-known reweighted zero-attracting least mean square algorithm (RZA-LMS) has been effective for the estimation of sparse system channels. However, the RZA-LMS algorithm utilizes a fixed step size to balance the steady-state mean square error and the convergence speed, resulting in a reduction in its performance. Thus, a trade-off between the convergence rate and the steady-state mean square error must be made. In this paper, utilizing the nonlinear relationship between the step size and the power of the noise-free prior error, a variable step-size strategy based on an error function is proposed. The simulation results indicate that the proposed variable step-size algorithm shows a better performance than the conventional RZA-LMS for both the sparse and the non-sparse systems.

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Acknowledgments

This work was supported by Program for ChangJiang Scholars and Innovative Research Team in University (IRT1299), and the special fund of Chongqing Key Laboratory (CSTC).

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Authors and Affiliations

  1. Chongqing Key Lab of Mobile Communications Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, People’s Republic of China

    Tao Fan & Yun Lin

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  1. Tao Fan
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  2. Yun Lin
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Correspondence to Tao Fan.

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Fan, T., Lin, Y. A Variable Step-Size Strategy Based on Error Function for Sparse System Identification. Circuits Syst Signal Process 36, 1301–1310 (2017). https://doi.org/10.1007/s00034-016-0344-1

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  • DOI: https://doi.org/10.1007/s00034-016-0344-1

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