Attention-based Reinforcement Learning for Real-Time UAV Semantic Communication
This addresses the problem of reliable and efficient UAV communication for applications like emergency services or IoT, representing a strong domain-specific advancement with competitive gains.
The paper tackles real-time control of multiple UAVs for ultra-reliable, low-latency air-to-ground communication by proposing GAXNet, a multi-agent deep reinforcement learning framework that uses attention graphs to coordinate UAVs and avoid collisions, achieving up to 4.5x higher rewards in training and 6.5x lower latency with a target error rate of 0.0000001 compared to a state-of-the-art baseline.
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 multi-agent 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, GAXNet achieves 6.5x lower latency with the target 0.0000001 error rate, compared to a state-of-the-art baseline framework.