Title
Active Coefficient Detection Maximum Correntropy Criterion Algorithm For Sparse Channel Estimation Under Non-Gaussian Environments
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.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2924028
IEEE ACCESS
Keywords
DocType
Volume
Channel estimation, Prediction algorithms, Convergence, Correlation, Kernel, Broadband communication, Estimation, Sparse channel estimation, maximum correntropy criterion, active coefficient detection, tap selection, impulsive noise environments
Journal
7
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Zeyang Sun101.01
Yingsong Li212034.72
Zhengxiong Jiang312.46
Wanlu Shi413.73