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• W4308702917 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." @default.
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• W4308702917 date "2022-10-31" @default.
• W4308702917 modified "2023-10-14" @default.
• W4308702917 title "Signal Detection in MIMO Communications System with Non-Gaussian Noises based on Deep Learning and Maximum Correntropy Criterion" @default.
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• W4308702917 doi "https://doi.org/10.5121/ijwmn.2022.14501" @default.
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