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Exact NLMS Algorithm with lp-Norm Constraint

delete2015-03-01
delete18
PRE
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L
Luis Weruaga *
S
Shihab Jimaa
DOI：10.1109/LSP.2014.2360889delete
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Abstract

Abstract

En 中文
This letter presents the exact normalized least-mean-square (NLMS) algorithm for the l(p)-norm-regularized square error, a popular choice for the identification of sparse systems corrupted by additive noise. The resulting exact l(p)-NLMS algorithm manifests differences to the original one, such as an independent update for each weight, a new sparsity-promoting compensated update, and the guarantee of stable convergence for any configuration (regardless the choice of 4 norm and sparsity-tradeoff constant). Simulation results show that the exact l(p)-NLMS is stable and it outperforms the original one, thus validating the optimality of the proposed methodology.
Keywords:
l(p)-norm constraint
normalized least mean square (NLMS) algorithm
Newton optimization
sparsity
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Journal

IEEE Signal Processing Magazine cover
IEEE Signal Processing Magazine
IF:
9.6
Papers:
1.1W
Citations:
1.7W

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