Optimization of Resource Allocation for Intelligent Reflecting Surface-enhanced Multi-UAV Assisted Semantic Communication
Unmanned Aerial Vehicles(UAV)present a cost-effective solution for wireless communication systems.This article introduces a novel Intelligent Reflecting Surface(IRS)to augment the semantic communication system among multiple UAVs.The system encompasses UAV equipped with IRS,Mobile Edge Computing(MEC)servers,and UAV featuring data collection and local semantic feature extraction functions.Optimizing signal reflection through IRS significantly enhances communication quality between drones and MEC servers.The formulated problem entails joint optimization of multiple drone trajectories,IRS reflection coefficients,and the number of semantic symbols to minimize transmission delays.To address this non-convex optimization problem,this paper introduces a Deep Reinforcement Learning(DRL)algorithm.Specifically,the Dueling Double Deep Q Network(D3QN)is employed to address discrete action space problems such as drone trajectory and semantic symbol quantity optimization.Additionally,Deep Deterministic Policy Gra-dient(DDPG)algorithm is utilized to solve continuous action space problems,such as IRS reflection coefficient optimization,enabling efficient decision-making.Simulation results demonstrate that the proposed intelligent optimization scheme outperforms various bench-mark schemes,particularly in scenarios with low transmission power.Furthermore,the intelligent optimization scheme proposed in this paper exhibits robust stability in response to power changes.
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