@inproceedings{0a2ef3df5757420d96d0c558f0360ff5,
title = "Machine Learning-based MIMO Signal Detection in Wireless Networks with Random Traffic",
abstract = "In this paper, we propose a novel machine learning-based signal detection scheme for multi-user wireless multiple-input multiple-output (MIMO) networks with random traffic. We focus on the challenging case in which the number of active users that transmit data to the base station in a time slot is a random variable from the viewpoint of the base station. Instead of using multiple machine learning models and exhaustive search, we propose using a novel deep machine learning model that adopts an extended constellation diagram and a loss function based on the nonuniform probability mass function for transmitted symbols. Simulation results reveal that the proposed machine learning-based signal detection scheme outperforms the zero-forcing detector and the minimum mean square error detector in wireless MIMO networks when the number of active users is random.",
keywords = "Multiple-input multiple-output, deep neural networks, machine learning, prior probability, random traffic, signal detection, wireless communications",
author = "Lai, {Po Yen} and Gau, {Rung Hung}",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE Wireless Communications and Networking Conference, WCNC 2022 ; Conference date: 10-04-2022 Through 13-04-2022",
year = "2022",
doi = "10.1109/WCNC51071.2022.9771685",
language = "English",
series = "IEEE Wireless Communications and Networking Conference, WCNC",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2298--2303",
booktitle = "2022 IEEE Wireless Communications and Networking Conference, WCNC 2022",
address = "United States",
}