TY - GEN
T1 - Attention-based reinforcement learning for real-time UAV semantic communication
AU - Yun, Won Joon
AU - Lim, Byungju
AU - Jung, Soyi
AU - Ko, Young Chai
AU - Park, Jihong
AU - Kim, Joongheon
AU - Bennis, Mehdi
N1 - Funding 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).
Publisher Copyright:
© 2021 IEEE
PY - 2021/9/6
Y1 - 2021/9/6
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85118152186&partnerID=8YFLogxK
U2 - 10.1109/ISWCS49558.2021.9562230
DO - 10.1109/ISWCS49558.2021.9562230
M3 - Conference contribution
AN - SCOPUS:85118152186
T3 - Proceedings of the International Symposium on Wireless Communication Systems
BT - 2021 17th International Symposium on Wireless Communication Systems, ISWCS 2021
PB - VDE Verlag GmbH
T2 - 17th International Symposium on Wireless Communication Systems, ISWCS 2021
Y2 - 6 September 2021 through 9 September 2021
ER -