Attention-based Reinforcement Learning for Real-Time UAV Semantic Communication

• URL: http://arxiv.org/abs/2105.10716v1
• Date: Sat, 22 May 2021 12:43:25 GMT
• Title: Attention-based Reinforcement Learning for Real-Time UAV Semantic Communication
• Authors: Won Joon Yun, Byungju Lim, Soyi Jung, Young-Chai Ko, Jihong Park, Joongheon Kim, Mehdi Bennis
• Abstract summary: We study the problem of air-to-ground ultra-reliable and low-latency communication (URLLC) for a moving ground user. We propose a novel multi-agent deep reinforcement learning framework, coined a graph attention exchange network (GAXNet) GAXNet achieves 6.5x lower latency with the target 0.0000001 error rate, compared to a state-of-the-art baseline framework.
• Score: 53.46235596543596
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

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