@inbook{ace316b1f0ff4165b58e75d7e3217451,
title = "Machine Learning for Predictive Deployment of UAVs with Rate Splitting Multiple Access",
abstract = "In this paper, an unmanned aerial vehicles (UAVs) deployment framework based on machine learning is studied. UAVs are deployed as flying base stations (BSs) to offload heavy traffic from ground BSs. A method of backpropagation neuron network (BP) algorithm is used to predict the future cellular traffic. According to the cellular traffic spatial distribution, a KEG algorithm, which is a joint K-means and expectation maximization (EM) algorithm based on Gaussian mixture model (GMM), is proposed for determining each UAV's service area. The UAV locations are optimized to minimize transmit power in their service area. Three multi-access techniques are compared to minimize the total uplink transmit power. Simulation results show that the proposed method can reduce up to 24% of the total power consumption compared to the conventional method without traffic prediction. Besides, rate splitting multiple access (RSMA) has the lower required transmit power compared to frequency domain multiple access (FDMA) and time domain multiple access (TDMA).",
keywords = "BP, EM, GMM, K-means, RSMA, UAV Deployment",
author = "Linyan Lu and Ye Hu and Yirun Zhang and Guangyu Jia and Jiangtian Nie and Mohammad Shikh-Bahaei",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.; 2020 IEEE Globecom Workshops, GC Wkshps 2020 ; Conference date: 07-12-2020 Through 11-12-2020",
year = "2020",
month = dec,
doi = "10.1109/GCWkshps50303.2020.9367523",
language = "English",
series = "2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings",
}