Optimum aerial base station deployment for UAV networks: A reinforcement learning approach

Meng Chun Hou, Der Jiunn Deng, Chia Ling Wu

研究成果: Conference contribution

1 引文 斯高帕斯(Scopus)

摘要

The boom of unmanned aerial vehicles (UAVs) is projected to fundamentally shift paradigms of transportations, logistics, agricultures, and public safety as a dominating unmanned application in following decades. To optimally process assigned tasks, each UAV requires prompt and ubiquitous information provisioning regarding the varying operation conditions, which renders exploiting base stations (BSs) of existing wireless infrastructures a tractable solution. To receive services from a BS, a UAV should stay within the coverage area of a BS, which however limits the operation range of a UAV. This obstacle thus drives the deployment of a special sort of UAV, known as an aerial base station (ABS), to relay signals between a BS and a UAV. Based on different flight paths of UAVs, an ABS should autonomously decide its own flight trajectory so as to maximize the number of UAVs which can receive wireless services. However, the inherently non-stationary environment renders the optimum autonomous deployment of an ABS a challenging issue. Inspired by the merit of interacting with the environment, we consequently propose a reinforcement learning scheme to optimize the flight trajectory of an ABS. To eliminate the engineering concern in the conventional Q-learning scheme that most state-action pairs may not be fully visited in the deployment of an ABS, in this paper, a state-amount-reduction (SAR) k-step Q-learning scheme is proposed to avoid the issue in the conventional Q-learning, so as to maximize the number of UAVs receiving services from an ABS. Through providing analytical foundations and simulation studies, outstanding performance of the proposed schemes is demonstrated as compared with that of the conventional reinforcement learning based ABS deployment.

原文English
主出版物標題2019 IEEE Globecom Workshops, GC Wkshps 2019 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781728109602
DOIs
出版狀態Published - 2019 十二月
事件2019 IEEE Globecom Workshops, GC Wkshps 2019 - Waikoloa, United States
持續時間: 2019 十二月 92019 十二月 13

出版系列

名字2019 IEEE Globecom Workshops, GC Wkshps 2019 - Proceedings

Conference

Conference2019 IEEE Globecom Workshops, GC Wkshps 2019
國家United States
城市Waikoloa
期間19-12-0919-12-13

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Hardware and Architecture
  • Software
  • Control and Optimization

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    Hou, M. C., Deng, D. J., & Wu, C. L. (2019). Optimum aerial base station deployment for UAV networks: A reinforcement learning approach. 於 2019 IEEE Globecom Workshops, GC Wkshps 2019 - Proceedings [9024648] (2019 IEEE Globecom Workshops, GC Wkshps 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GCWkshps45667.2019.9024648