Signal Detection in MIMO Communications System with Non-Gaussian Noises basedon Deep Learning and Maximum Correntropy Criterion
Year: 2024
Type: article
Abstract: 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
Source: Figshare
Author: Reza pourmir, Reza Monsefi and Ghosheh Abed Hodtani, Mohammad
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6977361472
FWCI:
Cites:
Cited by:
Related to: