Smooth Approximation l0-Norm Constrained Affine Projection Algorithm and Its Applications in Sparse Channel Estimation

We propose a smooth approximation l0-norm constrained affine projectionalgorithm (SL0-APA) to improve the convergence speed and the steady-state error of affine projectionalgorithm (APA) for sparse channel estimation. The proposed algorithm ensures improved performance interms of the convergence spe...

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Bibliographic Details
Published in:TheScientificWorld 2014, Vol.2014 (2014), p.1-15
Main Authors: Li, Yingsong, Hamamura, Masanori
Format: Article
Language:English
Subjects:
Algorithms
Communication
Computer simulation
Editors
Kalman filters
Mean square errors
NMR
Nuclear magnetic resonance
Queuing theory
Sparsity
Studies
Wireless communications
Online Access:Get full text
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Summary:We propose a smooth approximation l0-norm constrained affine projectionalgorithm (SL0-APA) to improve the convergence speed and the steady-state error of affine projectionalgorithm (APA) for sparse channel estimation. The proposed algorithm ensures improved performance interms of the convergence speed and the steady-state error via the combination of a smooth approximationl0-norm (SL0) penalty on the coefficients into the standard APA cost function, which gives rise to a zeroattractor that promotes the sparsity of the channel taps in the channel estimation and hence acceleratesthe convergence speed and reduces the steady-state error when the channel is sparse. The simulationresults demonstrate that our proposed SL0-APA is superior to the standard APA and its sparsity-awarealgorithms in terms of both the convergence speed and the steady-state behavior in a designated sparsechannel. Furthermore, SL0-APA is shown to have smaller steady-state error than the previously proposedsparsity-aware algorithms when the number of nonzero taps in the sparse channel increases.
ISSN:2356-6140
1537-744X
DOI:10.1155/2014/937252