Semantic-Aware UAV Swarms for Low-Delay and Energy-Efficient Data Collection
Uncrewed aerial vehicle (UAV) swarms performing data collection and transmission often face heavy communication loads and high end-to-end delay, which becomes more severe when processing large volumes of raw data. Semantic communication can alleviate this bottleneck by transmitting only task-relevant information instead of full raw data, thereby reducing the communication burden. Motivated by this, we propose a joint optimization framework that integrates semantic-aware communication and computation resource allocation for UAV swarms. In the considered scenario, member UAVs extract semantic features from raw data, forward them to the cluster head UAV, and finally transmit them to the base station, where data reconstruction is performed. A long-term average total delay minimization model is formulated, and a low-complexity algorithm is developed. Specifically, the long-term problem is reformulated into a per-slot deterministic structure guided by stability, and the subproblems are solved via an alternating refinement scheme with convex-tractable closed-form updates. Simulation results show that the proposed method consistently outperforms raw-data transmission and benchmark semantic schemes across diverse settings. In particular, it reduces average total delay by 14.94% and 15.33%, and improves energy efficiency by 28.32% and 28.18% under large swarm size and high data volume, respectively.