SPARLS: A Low Complexity Recursive $\mathcal{L}_1$-Regularized Least Squares Algorithm

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dc.creatorBabadi, Behtash
dc.creatorKalouptsidis, Nicholas
dc.creatorTarokh, Vahid
dc.date2009-01-06
dc.date.accessioned2026-07-07T12:27:07Z
dc.date.available2026-07-07T12:27:07Z
dc.descriptionWe develop a Recursive $\mathcal{L}_1$-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. Simulation studies in the context of channel estimation, employing 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.
dc.identifierhttps://arxiv.org/abs/0901.0734
dc.identifierhttp://arxiv.org/abs/0901.0734
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/215101
dc.subjectInformation Theory
dc.titleSPARLS: A Low Complexity Recursive $\mathcal{L}_1$-Regularized Least Squares Algorithm
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