UAV Positioning with Joint NOMA Power Allocation and Receiver Node Activation

Ahmad Gendia, Osamu Muta, Sherief Hashima, Kohei Hatano

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

This paper proposes reinforcement learning (RL)-based solutions for unmanned aerial vehicle (UAV) data offloading in B5G mmWave-enabled communications. This is particularly useful for ad-hoc transmission scenarios within environments experiencing connectivity issues with the main servicing network as in disaster-stricken areas. Double deep Q-network and multiarmed bandit-based algorithms are proposed to tackle the joint problem of UAV-positioning and Rx-node activation and power allocation for data offloading in downlink NOMA transmissions. Numerical simulations are performed to ensure the proposed RL-based algorithms can adequately provide high data transfer rates, along with random and exhaustive search solutions as benchmarks for lower and upper bounds on the achievable sum-rate levels.

Original languageEnglish
Title of host publication2022 IEEE 33rd Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages240-245
Number of pages6
ISBN (Electronic)9781665480536
DOIs
Publication statusPublished - 2022
Event33rd IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2022 - Virtual, Online, Japan
Duration: Sept 12 2022Sept 15 2022

Publication series

NameIEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC
Volume2022-September

Conference

Conference33rd IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2022
Country/TerritoryJapan
CityVirtual, Online
Period9/12/229/15/22

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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