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A Variable Step-Size Partial-Update Normalized Least Mean Square Algorithm for Second-Order Adaptive Volterra Filters

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Abstract

Partial-update (PU) algorithms offer reduced computational complexity to adaptive second-order Volterra filters (SOV) in nonlinear systems while retaining acceptable performance. In this paper, a new selective partial-update technique for the normalized LMS (NLMS) SOV algorithm is proposed, which requires lesser number of comparison operations per iteration than existing methods while providing close performance to the standard M-Max NLMS-SOV algorithm. Convergence properties of the proposed algorithm are enhanced by making the algorithm step-size time varying based on the natural logarithm function. Simulation experiments compare the proposed algorithm with existing PU and variable step-size NLMS-SOV algorithms, which illustrate the advantageous properties of the new algorithm. The proposed algorithm achieves both lower steady-state misalignment and faster convergence speed when compared with the fixed step-size full-update NLMS-SOV algorithm. Simulations also show that comparison operations overhead of the proposed algorithm is reduced significantly compared to that of the standard M-Max NLMS-SOV algorithm.

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Authors and Affiliations

  1. Department of Electrical Engineering, Jordan University of Science and Technology, Irbid, 22110, Jordan

    Khaled Mayyas & Liza Afeef

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  1. Khaled Mayyas
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  2. Liza Afeef
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Correspondence to Khaled Mayyas.

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Mayyas, K., Afeef, L. A Variable Step-Size Partial-Update Normalized Least Mean Square Algorithm for Second-Order Adaptive Volterra Filters. Circuits Syst Signal Process 39, 6073–6097 (2020). https://doi.org/10.1007/s00034-020-01446-2

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  • DOI: https://doi.org/10.1007/s00034-020-01446-2

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