Joint Optimization of Trajectory and Image Transmission in Multi-UAV Semantic Communication Networks.

Semantic communication is considered the key promoter and basic paradigm of future 6G networks and applications. In this paper, we investigate a multi-unmanned aerial vehicle (UAV) semantic communication framework, where the trajectories and communication services are jointly optimized for image transmission of ground users. We aim to minimize the transmission delay while considering constraints such as the UAV’s energy threshold, collision avoidance, and the bandwidth of the multi-UAV system. We propose a value decomposition based multi-agent deep reinforcement learning (VD-MADRL) algorithm to solve this problem, which explores the joint optimization scheme of UAV trajectory, channel allocation, and semantic information selection. Simulations demonstrate that the proposed algorithm greatly reduces system energy consumption and transmission delay compared to other traditional UAVs’ trajectory planning algorithms.