Partial-Update Normalized Sign LMS Algorithm Employing Sparse Updates

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概要

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• This paper provides a novel normalized sign least-mean square (NSLMS) algorithm which updates only a part of the filter coefficients and simultaneously performs sparse updates with the goal of reducing computational complexity. A combination of the partial-update scheme and the set-membership framework is incorporated into the context of L<SUB>∞</SUB>-norm adaptive filtering, thus yielding computational efficiency. For the stabilized convergence, we formulate a robust update recursion by imposing an upper bound of a step size. Furthermore, we analyzed a mean-square stability of the proposed algorithm for white input signals. Experimental results show that the proposed low-complexity NSLMS algorithm has similar convergence performance with greatly reduced computational complexity compared to the partial-update NSLMS, and is comparable to the set-membership partial-update NLMS.

著者

• CHOI Young-Seok

  Department of Electronic Engineering, Gangneung-Wonju National University

• SONG Woo-Jin

  Division of Electrical and Computer Engineering, Pohang University of Science and Technology (POSTECH)

• LEE Jae-Woo

  Division of Electronic and Computer Engineering, Pohang University of Science and Technology (POSTECH)

• KIM Seong-Eun

  Future IT Center, Samsung Advanced Institute of Technology (SAIT), Samsung Electronics

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• Partial-Update Normalized Sign LMS Algorithm Employing Sparse Updates

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