Improved adaptive sparse channel estimation using mixed square/fourth error criterion

Abstract

Sparse channel estimation problem is one of challenge technical issues in broadband wireless communications. Square error criterion based adaptive sparse channel estimation (SEC-ASCE) algorithms, e.g., zero-attracting least mean square (ZA-LMS) and reweighted ZA LMS (RZA-LMS), have been proposed to mitigate noises as well as to exploit the channel sparsity. However, the conventional SEC-ASCE algorithms are vulnerable to performance deteriorate due to 1) random scaling of input training signal, and 2) unable to balance between convergence speed and transient-state mean square error (MSE) performance. In this paper, a mixed square/fourth error criterion (SFEC) based ASCE algorithms (SEFC-ASCE), i.e., zero-attracting least mean square/fourth error (ZA-LMS/F) and reweighted ZA-LMS/F (RZA-LMS/F), are proposed to enhance estimation performance while without exhausting a lot computational complexity. First, regularization parameters of the ZA-LMS/F and RZA-LMS/F algorithms are selected by means of Monte-Carlo simulations. Second, lower bounds of the proposed channel estimation algorithms are derived and analyzed. Finally, simulation results are given to show that the proposed sparse LMS/F-type algorithms achieve better estimation performance than the conventional algorithms.

A Robust Sparse Adaptive Filtering Algorithm with a Correntropy Induced Metric Constraint for Broadband Multi-Path Channel Estimation

“…To address this problem, sparse adaptive filtering algorithms have been presented and proposed for sparse broadband channel estimation and sparse system identification applications [4][5][6][10][11][12][13][20][21][22][23][24][25][26][27][28][29]. In [11], a broadband sparse multi-path channel is used for the sparse channel estimation application, which is implemented by using sparse normalized LMS (NLMS) algorithms.…”

“…In [11], a broadband sparse multi-path channel is used for the sparse channel estimation application, which is implemented by using sparse normalized LMS (NLMS) algorithms. These sparse adaptive algorithms can be categorized into two groups, namely proportionate-type algorithms [20][21][22] and zero-attracting algorithms [4][5][6][10][11][12][13][23][24][25][26][27][28]. The proportionate-type algorithms aim to assign different weighting to each coefficient according to the magnitudes of the channel coefficients [20], which will increase the computational load.…”

“…After that, several variants of LMS based on zero attracting theory have been reported and used for channel estimation [5,6,23]. Although these sparse LMS algorithms can provide good channel estimation performance for ensuring the stability of the broadband wireless propagation, they are sensitive to the scaling of the inputs [24][25][26].…”

“…Consequently, the zero attracting techniques have been utilized to exploit sparse least mean fourth (LMF) [24], the sparse affine projection (AP) algorithm [12,13], sparse least mean square/fourth (LMS/F) [25][26][27] and other sparse adaptive filtering algorithms [28]. Although these sparse adaptive filter algorithms can effectively improve the performance of the LMS algorithms, some of them are computationally complex, and others should tradeoff the key parameter to give a balance in performance.…”

An Enhanced Set-Membership PNLMS Algorithm with a Correntropy Induced Metric Constraint for Acoustic Channel Estimation

“…However, the SM-NLMS algorithm cannot use the sparse characteristics of the multi-path channels. Then, the adaptive filtering algorithms for sparse channel estimation and sparse system identification applications have been proposed, including the proportionate NLMS (PNLMS) and the zero attracting adaptive filtering algorithms [16][17][18][19][20][21][22][23][24][25][26][27][28][29].…”