Multi-IRSs enhanced UAV secure semantic communication system

Abstract

This paper delves into a unmanned aerial vehicle (UAV) semantic communication system featuring multiple intelligent reflecting surfaces (IRSs). The UAV transmits semantic symbols to users in the presence of eavesdropping, and thus has inevitable security problems. In order to measure secure communication performance in semantic communication, the security semantic transmission rate (SSR) based on the semantic similarity is employed. An optimization problem is formulated to enhance the average SSR by optimizing UAV trajectory, IRS reflecting coefficients, number of semantic symbols, and transmission power, while satisfying position, speed, anti-collision, and power constraints. Due to the highly dynamic nature of the environment and the difficulties in solving complex modeling problems, a novel approach based on hybrid soft actor–critic (H-SAC) deep reinforcement learning (DRL) is proposed to address this non-convex optimization problem. The algorithm addresses the challenge of dynamic environments in UAV scenarios by seamlessly accommodating both discrete and continuous action spaces such as the trajectory and the IRS reflecting coefficient within the network. This enables effective processing of actions taken by intelligent agents, ensuring adaptability to the diverse requirements of the environment without the need for separate handling mechanisms. Simulation results show that the proposed intelligent algorithm exhibits excellent convergence and achieves a well-balanced pairing of appropriate IRSs and selection of flight paths, significantly improving the average SSR of the system. Specifically, our proposed scheme enhances the secure semantic transfer rate by more than fifty percent compared to when the number of semantic symbols is fixed at two.