Smooth approximation l(0)-norm constrained affine projection algorithm and its applications in sparse channel estimation.
We propose a smooth approximation l(0)-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 l(0)-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.
Graduate School of Engineering, Kochi University of Technology, Kami-shi 782-8502, Japan.
Yingsong
0000-0002-2450-6028
Y
Li
Graduate School of Engineering, Kochi University of Technology, Kami-shi 782-8502, Japan.
Masanori
0000-0001-6193-2037
M
Hamamura
D000465:Algorithms; D008962:Models, Theoretical