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

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

2009-01-06

arxiv.org/abs/0901.0734v1

Computer Science

Information Theory

Scientific paper

We 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.

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