Low-complexity proportionate algorithms with sparsity-promoting penalties

Abstract

There are two main families of algorithms that tackle the problem of sparse system identification: the proportionate family and the one that employs sparsity-promoting penalty functions. Recently, a new approach was proposed with the l0-IPAPA algorithm, which combines proportionate updates with sparsity-promoting penalties. This paper proposes some modifications to the l0-IPAPA algorithm in order to decrease its computational complexity while preserving its good convergence properties. Among these modifications, the inclusion of a data-selection mechanism provides promising results. Some enlightening simulation results are provided in order to verify and compare the performance of the proposed algorithms.