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.
|Title of host publication||2021 17th International Symposium on Wireless Communication Systems, ISWCS 2021|
|Publisher||VDE Verlag GmbH|
|Publication status||Published - 2021 Sept 6|
|Event||17th International Symposium on Wireless Communication Systems, ISWCS 2021 - Berlin, Germany|
Duration: 2021 Sept 6 → 2021 Sept 9
|Name||Proceedings of the International Symposium on Wireless Communication Systems|
|Conference||17th International Symposium on Wireless Communication Systems, ISWCS 2021|
|Period||21/9/6 → 21/9/9|
Bibliographical noteFunding Information:
This research was supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No.2018-0-00170, Virtual Presence in Moving Objects through 5G) and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1A4A1030775).
© 2021 IEEE
ASJC Scopus subject areas
- Computer Networks and Communications
- Electrical and Electronic Engineering