Deep Machine Learning in MIMO Communication Systems
Abstract
This paper presents an innovative approach to enhancing machine learning based communication systems, specifically focusing on multiple-input multiple-output (MIMO) configurations using autoencoders. We optimize the transmitter, receiver, and channel simultaneously under conditions of noise and channel fading, aiming to minimize the bit error rate (BER). By incorporating the Rayleigh fading channel a widely recognized model for wireless channel impairments into the autoencoder framework, we directly train the communication system to handle real world conditions. We introduce a novel optimization process tailored for deep learning-based MIMO communication, and thoroughly analyze the resulting BER performance across various signal to noise ratio (SNR) levels. Our simulation results reveal that the proposed end-to-end wireless communication system achieves significantly lower BER compared to conventional block-based processing methods, highlighting its potential for more efficient and reliable wireless communication.
Cite
@article{arxiv.2605.25458,
title = {Deep Machine Learning in MIMO Communication Systems},
author = {Mohammad Reza Ghavidel Aghdam and Alireza Naghavi},
journal= {arXiv preprint arXiv:2605.25458},
year = {2026}
}