# SPARLS: The Sparse RLS Algorithm

 Title: SPARLS: The Sparse RLS Algorithm Author: Babadi, Behtash; Kalouptsidis, Nicholas; Tarokh, Vahid Note: Order does not necessarily reflect citation order of authors. Citation: Babadi, Behtash, Nicholas Kalouptsidis, and Vahid Tarokh. 2010. SPARLS: The sparse RLS algorithm. IEEE Transactions on Signal Processing 58(8): 4013-4025. Full Text & Related Files: tsp_sparls_2010.pdf (341.4Kb; PDF) Abstract: We develop a recursive ${cal L}_{1}$-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. 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. Published Version: doi:10.1109/TSP.2010.2048103 Terms of Use: This article is made available under the terms and conditions applicable to Open Access Policy Articles, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#OAP Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:4687196

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Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University