AoI Minimization for UAV-to-Device Underlay Communication by Multi-agent Deep Reinforcement Learning

Fanyi Wu, Hongliang Zhang, Jianjun Wu, Lingyang Song, Zhu Han, H. Vincent Poor

Research output: Contribution to journalConference articlepeer-review

13 Scopus citations

Abstract

In this paper, we consider a cellular Internet of UAVs, where the sensory data can be transmitted either to the base station via cellular links, or to the mobile devices by underlay UAV-to-Device communications. To evaluate the freshness of the sensory data, the age of information (AoI) is adopted, in which a lower AoI implies fresher data. Since UAVs' AoIs are determined by their trajectories during sensing and transmission, we aim to minimize the AoIs of UAVs by designing their trajectories. This problem is a Markov decision problem with an infinite state-action space, and thus, we propose a multi-UAV trajectory design algorithm by leveraging multi-agent deep reinforcement learning to solve it. Simulation results show that our proposed algorithm outperforms both a greedy algorithm and a policy gradient algorithm.

Original languageEnglish (US)
Article number9322539
JournalProceedings - IEEE Global Communications Conference, GLOBECOM
DOIs
StatePublished - 2020
Externally publishedYes
Event2020 IEEE Global Communications Conference, GLOBECOM 2020 - Virtual, Taipei, Taiwan, Province of China
Duration: Dec 7 2020Dec 11 2020

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Signal Processing

Keywords

  • cellular Internet of UAVs
  • multi-agent deep reinforcement learning
  • UAV-to-Device communication

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