Multi-Hop UAV Relay Covert Communication: A Multi-Agent Reinforcement Learning Approach
Hengzhi Bai, Haichao Wang, Jiatao Du, Rongrong He, Guoxin Li, Yuhua Xu
Abstract
The unmanned aerial vehicle (UAV) network, due to its line-of-sight (LoS) communication characteristics, is highly susceptible to be eavesdropped. Covert communication is studied to protect the communication behaviour. This paper explores the joint optimization problem of trajectory and transmission power in a multi-hop UAV relay covert communication system. Considering the communication covertness, power constraints, and trajectory limitations, an algorithm based on multi-agent proximal policy optimization (MAPPO), named covert-MAPPO (C-MAPPO), is proposed. Leveraging the advantages of multi-agent reinforcement learning (MARL), this method optimizes a collaborative strategy for multiple UAVs regarding transmission power and flight trajectory to maximize system throughput while meeting covert constraints. Simulation results demonstrate that this algorithm outperforms benchmark algorithms in terms of throughput and reward convergence speed.