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Machine Learning for Predictive Deployment of UAVs with Rate Splitting Multiple Access

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Linyan Lu, Ye Hu, Yirun Zhang, Guangyu Jia, Jiangtian Nie, Mohammad Shikh-Bahaei

Original languageEnglish
Title of host publication2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728173078
DOIs
PublishedDec 2020
Event2020 IEEE Globecom Workshops, GC Wkshps 2020 - Virtual, Taipei, Taiwan
Duration: 7 Dec 202011 Dec 2020

Publication series

Name2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings

Conference

Conference2020 IEEE Globecom Workshops, GC Wkshps 2020
Country/TerritoryTaiwan
CityVirtual, Taipei
Period7/12/202011/12/2020

Bibliographical note

Publisher Copyright: © 2020 IEEE. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

King's Authors

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).

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