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

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 re...

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Bibliographic Details
Published in:Circuits, systems, and signal processing Vol. 39; no. 12; pp. 6073 - 6097
Main Authors: Mayyas, Khaled, Afeef, Liza
Format: Journal Article
Language:English
Published: New York Springer US 01.12.2020
Subjects:
Circuits and Systems
Electrical Engineering
Electronics and Microelectronics
Engineering
Instrumentation
Signal,Image and Speech Processing
Variable step size
Partial-update algorithms
Computational complexity
Adaptive second-order Volterra filters
NLMS algorithm
ISSN:0278-081X, 1531-5878
Online Access:Get full text
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Summary: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.
ISSN:0278-081X
1531-5878
DOI:10.1007/s00034-020-01446-2