Attention-based reinforcement learning for real-time UAV semantic communication

Won Joon Yun, Byungju Lim, Soyi Jung, Young Chai Ko, Jihong Park, Joongheon Kim, Mehdi Bennis

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

Abstract

In this article, we study the problem of air-to-ground ultra-reliable and low-latency communication (URLLC) for a moving ground user. This is done by controlling multiple unmanned aerial vehicles (UAVs) in real time while avoiding inter-UAV collisions. To this end, we propose a novel multiagent deep reinforcement learning (MADRL) framework, coined a graph attention exchange network (GAXNet). In GAXNet, each UAV constructs an attention graph locally measuring the level of attention to its neighboring UAVs, while exchanging the attention weights with other UAVs so as to reduce the attention mismatch between them. Simulation results corroborates that GAXNet achieves up to 4.5x higher rewards during training. At execution, without incurring inter-UAV collisions, G2ANet improves reliability of air-to-ground network in terms of latency and error rate.

Original languageEnglish
Title of host publication2021 17th International Symposium on Wireless Communication Systems, ISWCS 2021
PublisherVDE Verlag GmbH
ISBN (Electronic)9781728174327
DOIs
Publication statusPublished - 2021 Sep 6
Event17th International Symposium on Wireless Communication Systems, ISWCS 2021 - Berlin, Germany
Duration: 2021 Sep 62021 Sep 9

Publication series

NameProceedings of the International Symposium on Wireless Communication Systems
Volume2021-September
ISSN (Print)2154-0217
ISSN (Electronic)2154-0225

Conference

Conference17th International Symposium on Wireless Communication Systems, ISWCS 2021
Country/TerritoryGermany
CityBerlin
Period21/9/621/9/9

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering
  • Communication

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