Reinforcement Learning for a Cellular Internet of UAVs: Protocol Design, Trajectory Control, and Resource Management

Jingzhi Hu, Hongliang Zhang, Lingyang Song, Zhu Han, H. Vincent Poor

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

Unmanned aerial vehicles (UAVs) can be powerful Internet of Things components to execute sensing tasks over the next-generation cellular networks, which are generally referred to as the cellular Internet of UAVs. However, due to the high mobility of UAVs and shadowing in airto-ground channels, UAVs operate in a dynamic and uncertain environment. Therefore, UAVs need to improve the quality of service of sensing and communication without complete information, which makes reinforcement learning suitable for use in the cellular Internet of UAVs. In this article, we propose a distributed sense-and-send protocol to coordinate UAVs for sensing and transmission. Then we apply reinforcement learning in the cellular Internet of UAVs to solve key problems such as trajectory control and resource management. Finally, we point out several potential future research directions.

Original languageEnglish (US)
Article number9023933
Pages (from-to)116-123
Number of pages8
JournalIEEE Wireless Communications
Volume27
Issue number1
DOIs
StatePublished - Feb 1 2020

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering

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