Low-Complexity Non-Uniform Penalized Affine Projection Algorithm for Sparse System Identification

Abstract

In this paper, an improved sparse-aware affine projection (AP) algorithm for sparse system identification is proposed and investigated. The proposed sparse AP algorithm is realized by integrating a non-uniform norm constraint into the cost function of the conventional AP algorithm, which can provide a zero attracting on the filter coefficients according to the value of each filter coefficient. Low complexity is obtained by using a linear function instead of the reweighting term in the modified AP algorithm to further improve the performance of the proposed sparse AP algorithm. The simulation results demonstrate that the proposed sparse AP algorithm outperforms the conventional AP and previously reported sparse-aware AP algorithms in terms of both convergence speed and steady-state error when the system is sparse.

Norm Penalized Joint-Optimization NLMS Algorithms for Broadband Sparse Adaptive Channel Estimation

“…We construct several experiments to look into the estimation behavior of our ZAJO-NLMS and RZAJO-NLMS algorithms through a multi-path wireless communication channel, which is a general sparse channel model obtained from the measurement [14,17] and which has been widely used for verifying the estimation performance of NLMS-based channel estimations [6,7,9,12,14,24,[27][28][29][30][31][32][33][34][35][36][37][38][39][40]. Moreover, the channel estimation behavior is evaluated using mean-square error, and the channel estimation performance is also compared with the traditional LMS, NLMS, ZA-LMS, RZA-LMS, ZA-NLMS and RZA-NLMS algorithms.…”

“…Subsequently, the zero attracting techniques have been widely researched, and a great quantity of sparse LMS algorithms was exploited by using different norm constraints, such as l p -norm and smooth approximation l 0 -norm constraints [28][29][30][31][32]. Furthermore, the zero attracting (ZA) technique has also been expanded into the affine projection algorithm and the normalized NLMS algorithms to further exploit the applications of the ZA algorithms [12,[33][34][35][36][37][38][39][40], which includes ZA-NLMS and RZA-NLMS algorithms. However, the affine projection algorithm has higher complexity than the NLMS algorithm, which limited its applications.…”

“…Their estimation behaviors are discussed via a broadband multi-path channel, which is a prior known sparse channel from the previous studies [6,7,9,12,14,24,[27][28][29][30][31][32][33][34][35][36][37][38][39][40]. The proposed algorithms are realized by implementing a joint-optimization on the regularization parameter and step size to achieve a minimization of the channel estimation misalignment, where the joint-optimization method is obtained from [41].…”

“…The joint-optimization scheme is derived in detail in the context of zero attraction theory, and it is incorporated into the previously-proposed ZA-and RZA-NLMS algorithms to form zero-attracting joint-optimization NLMS (ZAJO-NLMS) and reweighted ZAJO-NLMS (RZAJO-NLMS) algorithms. The parameter selection and the behavior of our ZAJO-NLMS and RZAJO-NLMS are evaluated though a sparse channel, which is similar to [6,7,9,12,14,24,[27][28][29][30][31][32][33][34][35][36][37][38][39][40]. The computer simulations indicate that our ZAJO-NLMS and RZAJO-NLMS algorithms perform better than the previously-presented ZA-NLMS, RZA-NLMS, ZA-LMS, RZA-LMS, traditional LMS and NLMS algorithms for dealing with sparse channel estimation.…”

A sparsity-aware proportionate normalized maximum correntropy criterion algorithm for sparse system identification in non-Gaussian environment

“…However, most of the adaptive filter algorithms are mainly presented for non-sparse systems and Gaussian noise environment. To estimate the sparse channels, a lot of adaptive filter algorithms were reported [7], [8], [9], [10], [11], [12], [13], [14] to estimate sparse channels. Least mean square (LMS) algorithm [7] has attracted much attention owing to its simple and low computational complexity.…”