Joint Optimization of Hovering Position and Resource Allocation in UAV-Enabled Semantic Communications via Greedy-Enhanced Adaptive Cellular Genetic Algorithm

Despite significant advancements in communication systems, inherent limitations persist in providing reliable data transmission for emerging applications with massive data exchanges. Semantic communication offers promising solutions by extracting and transmitting meaningful information rather than raw bit sequences. However, it faces challenges from high mobility and dynamic channel conditions in wireless environments. In this paper, we design a ground-to-air network architecture that integrates a rotary-wing unmanned aerial vehicle (UAV) and ground terminals to maximize semantic transmission efficiency while maintaining low energy consumption. This approach leverages the high mobility of the UAV for flexible deployment and the data reduction capabilities of semantic communication. Therefore, we formulate a multi-objective optimization problem to simultaneously balance the total semantic transmission rate and the UAV propulsion energy consumption by jointly optimizing the UAV hovering position, semantic encoding lengths, and resource block (RB) allocation. The problem is complex, with mixed continuous and discrete variables, which necessitates an advanced optimization method. To address these challenges, we propose a novel greedy-enhanced adaptive multi-objective cellular genetic algorithm (GEAMOCell), which utilizes an adaptive neighborhood selection mechanism to balance exploration and exploitation, and employs a crowding-guided archive feedback mechanism to maintain population diversity. The simulation results demonstrate that the proposed GEAMOCell algorithm outperforms baseline algorithms in terms of convergence, semantic transmission rate, and energy efficiency.