SPARLS: The sparse RLS algorithm

Published

Journal Article

We develop a recursive L1-regularized least squares (SPARLS) algorithm for the estimation of a sparse tap-weight vector in the adaptive filtering setting. The SPARLS algorithm exploits noisy observations of the tap-weight vector output stream and produces its estimate using an expectation-maximization type algorithm. We prove the convergence of the SPARLS algorithm to a near-optimal estimate in a stationary environment and present analytical results for the steady state error. Simulation studies in the context of channel estimation, employing multipath wireless channels, show that the SPARLS algorithm has significant improvement over the conventional widely used recursive least squares (RLS) algorithm in terms of mean squared error (MSE). Moreover, these simulation studies suggest that the SPARLS algorithm (with slight modifications) can operate with lower computational requirements than the RLS algorithm, when applied to tap-weight vectors with fixed support. © 2010 IEEE.

Full Text

Duke Authors

Cited Authors

  • Babadi, B; Kalouptsidis, N; Tarokh, V

Published Date

  • August 1, 2010

Published In

Volume / Issue

  • 58 / 8

Start / End Page

  • 4013 - 4025

International Standard Serial Number (ISSN)

  • 1053-587X

Digital Object Identifier (DOI)

  • 10.1109/TSP.2010.2048103

Citation Source

  • Scopus