Robust sparse channel estimation and equalization in impulsive noise using linear programming

2013 ◽  
Vol 93 (5) ◽  
pp. 1095-1105 ◽  
Author(s):  
Xue Jiang ◽  
T. Kirubarajan ◽  
Wen-Jun Zeng
Keyword(s):  
Linear Programming ◽  
Channel Estimation ◽  
Impulsive Noise ◽  
Sparse Channel Estimation ◽  
Channel Estimation And Equalization ◽  
Sparse Channel

scholarly journals Maximum Correntropy Criterion Based l1-Iterative Wiener Filter for Sparse Channel Estimation Robust to Impulsive Noise

2020 ◽  
Vol 10 (3) ◽  
pp. 743
Author(s):  
Junseok Lim
Keyword(s):  
Channel Estimation ◽  
Covariance Matrix ◽  
Impulsive Noise ◽  
Wiener Filter ◽  
Penalty Term ◽  
Sparse Channel Estimation ◽  
Convex Regularization ◽  
Maximum Correntropy Criterion ◽  
Auto Covariance ◽  
Sparse Channel

In this paper, we propose a new sparse channel estimator robust to impulsive noise environments. For this kind of estimator, the convex regularized recursive maximum correntropy (CR-RMC) algorithm has been proposed. However, this method requires information about the true sparse channel to find the regularization coefficient for the convex regularization penalty term. In addition, the CR-RMC has a numerical instability in the finite-precision cases that is linked to the inversion of the auto-covariance matrix. We propose a new method for sparse channel estimation robust to impulsive noise environments using an iterative Wiener filter. The proposed algorithm does not need information about the true sparse channel to obtain the regularization coefficient for the convex regularization penalty term. It is also numerically more robust, because it does not require the inverse of the auto-covariance matrix.

A Low-Complexity Sparse Channel Estimation Method for OFDM Systems

2010 ◽  
Vol E93-B (8) ◽  
pp. 2211-2214
Author(s):  
Bin SHENG ◽  
Pengcheng ZHU ◽  
Xiaohu YOU ◽  
Lan CHEN
Keyword(s):  
Channel Estimation ◽  
Estimation Method ◽  
Low Complexity ◽  
Ofdm Systems ◽  
Sparse Channel Estimation ◽  
Sparse Channel
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