Additive non-Gaussian noise channel estimation by using minimum error entropy criterion

2017 ◽  
Author(s):  
Ahmad Reza Heravi ◽  
Ghosheh Abed Hodtani
Keyword(s):  
Channel Estimation ◽  
Gaussian Noise ◽  
Minimum Error ◽  
Non Gaussian ◽  
Entropy Criterion

Robust correntropy ICA based blind channel estimation for MIMO-OFDM systems

2015 ◽  
Vol 34 (3) ◽  
pp. 962-978
Author(s):  
Khaled Abdulaziz Alaghbari ◽  
Lim Heng Siong ◽  
Alan W.C. Tan
Keyword(s):  
Channel Estimation ◽  
Simulation Study ◽  
Gaussian Noise ◽  
Blind Channel Estimation ◽  
Channel Estimator ◽  
Content Type ◽  
Comparison Results ◽  
Blind Channel ◽  
Mimo Ofdm ◽  
Non Gaussian

Purpose – The purpose of this paper is to propose a robust correntropy assisted blind channel estimator for multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) for improved channel gains estimation and channel ordering and sign ambiguities resolution in non-Gaussian noise channel. Design/methodology/approach – The correntropy independent component analysis with L1-norm cost function is used for blind channel estimation. Then a correntropy-based method is formulated to resolve the sign and order ambiguities of the channel estimates. Findings – Simulation study on Gaussian noise scenario shows that the proposed method achieves almost the same performance as the conventional L2-norm based method. However, in non-Gaussian noise scenarios performance of the proposed method significantly outperforms the conventional and other popular estimators in terms of mean square error (MSE). To solve the ordering and sign ambiguities problems, an auto-correntropy-based method is proposed and compared with the extended cross-correlation-based method. Simulation study shows improved performance of the proposed method in terms of MSE. Originality/value – This paper presents for the first time, a correntropy-based blind channel estimator for MIMO-OFDM as well as simulated comparison results with traditional correlation-based methods in non-Gaussian noise environment.

scholarly journals ʅ1-Norm Constrained Minimum Eror Entropy Algorithm

2020 ◽  
Vol 9 (3) ◽  
pp. 2350-2354
Keyword(s):  
Linear Constraint ◽  
Channel Noise ◽  
Gaussian Channel ◽  
Linear Phase ◽  
Minimum Error ◽  
Phase Condition ◽  
Sparse System Identification ◽  
Non Gaussian ◽  
High Order Statistics ◽  
Entropy Criterion

This work proposes a linear phase sparse minimum error entropy adaptive filtering algorithm. The linear phase condition is obtained by considering symmetry or anti symmetry condition onto the system coefficients. The proposed work integrates linear constraint based on linear phase of the system and -norm for sparseness into minimum error entropy adaptive algorithm. The proposed -norm linear constrained minimum error entropy criterion ( -CMEE) algorithm makes use of high-order statistics, hence worthy for non-Gaussian channel noise. The experimental results obtained for linear phase sparse system identification in the presence of non-Gaussian channel noise reveal that the proposed algorithm has lower steady state error and higher convergence rate than other existing MEE variants.

scholarly journals 3D-MIMO Channel Estimation under Non-Gaussian Noise with Unknown PDF

Electronics ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 316 ◽  
Author(s):  
Wei Chen ◽  
Feng Li ◽  
Yiting Peng
Keyword(s):  
Channel Estimation ◽  
Gaussian Noise ◽  
Additive White Gaussian Noise ◽  
Gaussian Mixture ◽  
Mimo Channel ◽  
Estimation Model ◽  
Channel Matrix ◽  
Common Support ◽  
3D Mimo ◽  
Non Gaussian

Three-dimensional-multiple-input-multiple-output (3D-MIMO) technology has attracted a lot of attention in the field of wireless communication. Most of the research mainly focuses on channel estimation model which is affected by additive-white-Gaussian-noise (AWGN). However, under the influence of some specified factors, such as electronic interference and man-made noise, the noise of the channel does not follow the Gaussian distribution anymore. Sometimes, the probability density function (PDF) of the noise is unknown at the receiver. Based on this reality, this paper tries to address the problem of channel estimation under non-Gaussian noise with unknown PDF. Firstly, the common support of angle domain channel matrix is estimated by compressed sensing (CS) reconstruction algorithm and a decision rule. Secondly, after modeling the received signal as a Gaussian mixture model (GMM), a data pruning algorithm is exerted to calculate the order of GMM. Lastly, an expectation maximization (EM) algorithm for linear regression is implemented to estimate the the channel matrix iteratively. Furthermore, sparsity, not only in the time domain, but in addition in the angle domain, is utilized to improve the channel estimation performance. The simulation results demonstrate the merits of the proposed algorithm compared with the traditional ones.

scholarly journals A Robust Diffusion Estimation Algorithm with Self-adjusting Step-size in WSNs

2017 ◽  
Author(s):  
Feng Chen ◽  
Qing Ye ◽  
Xiaodan Shao ◽  
Shukai Duan
Keyword(s):  
Estimation Algorithm ◽  
Wireless Sensor ◽  
Step Size ◽  
Local Data ◽  
Global Parameter ◽  
Minimum Error ◽  
Fast Speed ◽  
Optimal Estimator ◽  
Non Gaussian ◽  
Entropy Criterion

In wireless sensor networks (WSNs), each sensor node can estimate the global parameter from the local data in distributed manner. This paper proposed a robust diffusion estimation algorithm based on minimum error entropy criterion with self-adjusting step-size, which are referred to as diffusion MEE-SAS (DMEE-SAS) algorithm. The DMEE-SAS algorithm has fast speed of convergence and is robust against non-Gaussian noise in the measurements. The detailed performance analysis of the DMEE-SAS algorithm is performed. By combining the DMEE-SAS with diffusion minimum error entropy (DMEE) algorithms, an Improving DMEE-SAS algorithm is proposed, in non-stationary environment where tracking is very important. The Improving DMEE-SAS algorithm can avoid insensitivity of the DMEE-SAS algorithm due to the small effective step-size near the optimal estimator, and obtain a fast convergence speed. Numerical simulations are given to verify the effectiveness and advantages of these proposed algorithms.

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