Volume 14, Number 5

Signal Detection in MIMO Communications System with Non-Gaussian Noises based
on Deep Learning and Maximum Correntropy Criterion


Mohammad Reza pourmir, Reza Monsefi and Ghosheh Abed Hodtani, Computer Department, Engineering Faculty Ferdowsi University of Mashhad (FUM), Iran


In this paper, we study signal detection in multi-input-multi output (MIMO) communications system with non-Gaussian noises such as Middleton Class A noise, Gaussian mixtures and alpha stable distributions, using several deep neural network-based detector models such as FULLYCONNECTED and DETNET detector. By applying information theoretic criterion of Maximum Correntropy , SVD analysis on the channel matrix and reducing network complexity, the suggested deep neural network detector performs well in environments with non-Gaussian noises and, compared to the deep neural network-based detector with MSE loss function, achieves better performance.


Signal Detection, MIMO, Deep Learning, Information theory.