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
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.