Channel Estimation Using Adaptive Cuckoo Search Based Wiener Filter

2020 ◽  
pp. 332-346
Author(s):  
Gopala Krishna Mellempudi ◽  
Vinay Kumar Pamula
Keyword(s):  
Channel Estimation ◽  
Wiener Filter ◽  
Cuckoo Search

scholarly journals Wiener Filter versus Recurrent Neural Network-based 2D-Channel Estimation for V2X Communications

2021 ◽  
Author(s):  
Moritz Benedikt Fischer ◽  
Sebastian Dorner ◽  
Sebastian Cammerer ◽  
Takayuki Shimizu ◽  
Bin Cheng ◽  
...  
Keyword(s):  
Neural Network ◽  
Channel Estimation ◽  
Recurrent Neural Network ◽  
Wiener Filter

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.

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