2016 Second International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN 2016)

29 / 2016-08-21 00:34:29

Additive Non-Gaussian Noise Channel Estimation by Using Minimum Error Entropy Criterion

931,5613,10699,1152,11190

channel estimation is an important component of wireless communications. This paper deals with the comparison between Mean Square Error (MSE) and Minimum Error Entropy (MEE) methods in additive non-Gaussian noise channel estimation. This essay analyzes MEE and MSE algorithms in several channel models utilizing Neural Networks. The aim of this study is first to compare the performance of an extended MSE algorithm with MEE method. The trained neural networks can be applied as an equalizer in the receiver. Moreover, to do a complete comparison between methods, we compare them in both low and high SNR regimes. The numerical results illustrate that MEE algorithm is more capable than the MSE based algorithm for channel estimation. In fact, with additive non-Gaussian noise the performance of MSE method is approximately as same as the MEE based approach results for high SNR regime, but the MEE outperforms MSE based method obviously for low SNR regime with non-Gaussian noise.