scholarly journals Steady-State Mean Square Performance of the Multichannel Filtered-X Affine Projection Algorithm

2012 ◽  
Vol 60 (6) ◽  
pp. 2771-2785 ◽  
Author(s):  
Miguel Ferrer ◽  
Maria de Diego ◽  
Alberto Gonzalez ◽  
Gema Piñero
Keyword(s):  
Steady State ◽  
Projection Algorithm ◽  
Mean Square ◽  
Affine Projection ◽  
Affine Projection Algorithm

scholarly journals Sparse Adaptive Channel Estimation Based onlp-Norm-Penalized Affine Projection Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yingsong Li ◽  
Wenxing Li ◽  
Wenhua Yu ◽  
Jian Wan ◽  
Zhiwei Li
Keyword(s):  
Steady State ◽  
Channel Estimation ◽  
Convergence Speed ◽  
Projection Algorithm ◽  
Broadband Wireless ◽  
Sparse Channel Estimation ◽  
Affine Projection ◽  
Affine Projection Algorithm ◽  
The Cost ◽  
Steady State Performance

We propose anlp-norm-penalized affine projection algorithm (LP-APA) for broadband multipath adaptive channel estimations. The proposed LP-APA is realized by incorporating anlp-norm into the cost function of the conventional affine projection algorithm (APA) to exploit the sparsity property of the broadband wireless multipath channel, by which the convergence speed and steady-state performance of the APA are significantly improved. The implementation of the LP-APA is equivalent to adding a zero attractor to its iterations. The simulation results, which are obtained from a sparse channel estimation, demonstrate that the proposed LP-APA can efficiently improve channel estimation performance in terms of both the convergence speed and steady-state performance when the channel is exactly sparse.

scholarly journals Steady-State Performance of an Adaptive Combined MISO Filter Using the Multichannel Affine Projection Algorithm

Algorithms ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 2
Author(s):  
Danilo Comminiello ◽  
Michele Scarpiniti ◽  
Luis Azpicueta-Ruiz ◽  
Aurelio Uncini
Keyword(s):  
Steady State ◽  
Adaptive Filters ◽  
Single Channel ◽  
Projection Algorithm ◽  
Adaptive Combination ◽  
Affine Projection ◽  
Affine Projection Algorithm ◽  
Single Output ◽  
Combination Scheme ◽  
Multiple Input

The combination of adaptive filters is an effective approach to improve filtering performance. In this paper, we investigate the performance of an adaptive combined scheme between two adaptive multiple-input single-output (MISO) filters, which can be easily extended to the case of multiple outputs. In order to generalize the analysis, we consider the multichannel affine projection algorithm (APA) to update the coefficients of the MISO filters, which increases the possibility of exploiting the capabilities of the filtering scheme. Using energy conservation relations, we derive a theoretical behavior of the proposed adaptive combination scheme at steady state. Such analysis entails some further theoretical insights with respect to the single channel combination scheme. Simulation results prove both the validity of the theoretical steady-state analysis and the effectiveness of the proposed combined scheme.

scholarly journals Smooth Approximationl0-Norm Constrained Affine Projection Algorithm and Its Applications in Sparse Channel Estimation

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Yingsong Li ◽  
Masanori Hamamura
Keyword(s):  
Steady State ◽  
Channel Estimation ◽  
Convergence Speed ◽  
Projection Algorithm ◽  
Smooth Approximation ◽  
Sparse Channel Estimation ◽  
Affine Projection ◽  
Affine Projection Algorithm ◽  
Sparse Channel ◽  
State Error

We propose a smooth approximationl0-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 approximationl0-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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