A Modified Recursive Regularization Factor Calculation for Sparse RLS Algorithm with l 1 -Norm

Junseok Lim, Keunhwa Lee and Seokjin Lee
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Junseok Lim: Department of Electrical Engineering, College of Electronics and Information Engineering, Sejong University, Gwangjin-gu, Seoul 05006, Korea
Keunhwa Lee: Department of Defense Systems Engineering, College of Engineering, Sejong University, Gwangjin-gu, Seoul 05006, Korea
Seokjin Lee: School of Electronics Engineering, School of Electronic and Electrical Engineering, College of IT Engineering, Kyungpook National University, Daegu 41566, Korea

Mathematics, 2021, vol. 9, issue 13, 1-9

Abstract: In this paper, we propose a new calculation method for the regularization factor in sparse recursive least squares (SRLS) with l 1 -norm penalty. The proposed regularization factor requires no prior knowledge of the actual system impulse response, and it also reduces computational complexity by about half. In the simulation, we use Mean Square Deviation (MSD) to evaluate the performance of SRLS, using the proposed regularization factor. The simulation results demonstrate that SRLS using the proposed regularization factor calculation shows a difference of less than 2 dB in MSD from SRLS, using the conventional regularization factor with a true system impulse response. Therefore, it is confirmed that the performance of the proposed method is very similar to that of the existing method, even with half the computational complexity.

Keywords: sparse impulse response system; sparse system estimation; l 1 -RLS; regularization factor (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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