Smooth Approximation l0-Norm Constrained Affine Projection Algorithm and Its Applications in Sparse Channel Estimation
We propose a smooth approximation l0-norm constrained affine projection algorithm (SL0-APA) to improve the convergence speed and the steady-state error of affine projection algorithm (APA) for sparse channel estimation. The proposed algorithm ensures improved performance in terms of the convergence...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/937252 |
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| Summary: | We propose a smooth approximation l0-norm constrained affine projection
algorithm (SL0-APA) to improve the convergence speed and the steady-state error of affine projection
algorithm (APA) for sparse channel estimation. The proposed algorithm ensures improved performance in
terms of the convergence speed and the steady-state error via the combination of a smooth approximation
l0-norm (SL0) penalty on the coefficients into the standard APA cost function, which gives rise to a zero
attractor that promotes the sparsity of the channel taps in the channel estimation and hence accelerates
the convergence speed and reduces the steady-state error when the channel is sparse. The simulation
results demonstrate that our proposed SL0-APA is superior to the standard APA and its sparsity-aware
algorithms in terms of both the convergence speed and the steady-state behavior in a designated sparse
channel. Furthermore, SL0-APA is shown to have smaller steady-state error than the previously proposed
sparsity-aware algorithms when the number of nonzero taps in the sparse channel increases. |
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| ISSN: | 2356-6140 1537-744X |