UAV Positioning with Joint NOMA Power Allocation and Receiver Node Activation

Ahmad Gendia, Osamu Muta, Sherief Hashima, Kohei Hatano

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

3 被引用数 (Scopus)

抄録

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.

本文言語英語
ホスト出版物のタイトル2022 IEEE 33rd Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2022
出版社Institute of Electrical and Electronics Engineers Inc.
ページ240-245
ページ数6
ISBN(電子版)9781665480536
DOI
出版ステータス出版済み - 2022
イベント33rd IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2022 - Virtual, Online, 日本
継続期間: 9月 12 20229月 15 2022

出版物シリーズ

名前IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC
2022-September

会議

会議33rd IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2022
国/地域日本
CityVirtual, Online
Period9/12/229/15/22

!!!All Science Journal Classification (ASJC) codes

  • 電子工学および電気工学

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