Abstract
In this paper, a kind of active coefficient detection (ACD)-based maximum correntropy criterion (MCC) algorithm is proposed to estimate a sparse multi-path channel under the non-Gaussian environments. The proposed ACD-based MCC algorithms are realized by developing an active coefficient detection mechanism, which can distinguish the active taps within the sparse channels and find out the position and the number of active taps. Therefore, only the active taps coefficient is updated while the trivial channel coefficients are set to be zeros. Various computer simulation experiments are carried out to investigate the performance of the proposed ACD-based MCC algorithms under different impulsive noises. The achieved simulation results prove that the proposed ACD-based MCC algorithms are effective and outperform the previous adaptive filtering algorithms for the sparse channel estimation with regard to both the convergence and the estimation error.
Highlights
With the growth of wireless communication technologies, broadband information transmission has become a hot topic to meet the demand of users [1]
The active coefficient detection based maximum correntropy criterion (ACD-based MCC) algorithms which consist of ACD-MCC algorithm and ACD-normalized MCC (ACD-NMCC) algorithm are proposed and discussed to develop a sparse adaptive channel estimations (ACEs) for estimating sparse channels under the impulsive noises
The white Gaussian signal is implemented using a zero-mean Gaussian distribution random signal whose variance is σx2 = 1, while the colored signal is realized by x(n) = 0.8x(n − 1) + z(n), where z(n) is a discrete white zero-mean unit-variance progress
Summary
With the growth of wireless communication technologies, broadband information transmission has become a hot topic to meet the demand of users [1]. In recent years, another kind of sparse ACE methods [8], [10]–[12] have been put forward on the basis of the zero-attracting (ZA) scheme which is enlightened by the recent invention in compressive sensing (CS) [25] Based on this idea, the ZA-LMS and the reweighted ZA-LMS (RZA-LMS) algorithms [26] have been put forward via integrating a sparsity-aware penalty on channel coefficient vector. The active coefficient detection based maximum correntropy criterion (ACD-based MCC) algorithms which consist of ACD-MCC algorithm and ACD-normalized MCC (ACD-NMCC) algorithm are proposed and discussed to develop a sparse ACE for estimating sparse channels under the impulsive noises. The proposed ACD-based MCC algorithms are realized and implemented by utilizing an ACD criterion which improves the ability to predict the non-zero coefficients in the unknown channels.
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