Comparison of SPARLS and RLS algorithms for adaptive filtering

Authors:
Behtash Babadi;Nicholas Kalouptsidis;Vahid Tarokh
Affiliations:
School of Engineering and Applied Sciences, Harvard University, Cambridge, MA;Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens, Greece;School of Engineering and Applied Sciences, Harvard University, Cambridge, MA
Venue:
SARNOFF'09 Proceedings of the 32nd international conference on Sarnoff symposium
Year:
2009

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Abstract

In this paper, we overview the Low Complexity Recursive L1-Regularized Least Squares (SPARLS) algorithm proposed in [2], for the estimation of sparse signals in an adaptive filtering setting. The SPARLS algorithm is based on an Expectation-Maximization type algorithm adapted for online estimation. Simulation results for the estimation of multi-path wireless channels show that the SPARLS algorithm has significant improvement over the conventional widely-used Recursive Least Squares (RLS) algorithm, in terms of both mean squared error (MSE) and computational complexity.