In this paper, we aim to explore the use of uplink semantic communications with the assistance of UAV in order to improve data collection effiicency for metaverse users in remote areas. To reduce the time for uplink data collection while balancing the trade-off between reconstruction quality and computational energy cost, we propose a hybrid action reinforcement learning (RL) framework to make decisions on semantic model scale, channel allocation, transmission power, and UAV trajectory. The variables are classified into discrete type and continuous type, which are optimized by two different RL agents to generate the combined action. Simulation results indicate that the proposed hybrid action reinforcement learning framework can effectively improve the efficiency of uplink semantic data collection under different parameter settings and outperforms the benchmark scenarios.

翻译：本文旨在探索利用无人机辅助的上行语义通信，以提升偏远地区元宇宙用户的数据收集效率。为减少上行数据收集时间，同时平衡重建质量与计算能耗之间的权衡，我们提出了一种混合动作强化学习框架，用于对语义模型规模、信道分配、传输功率及无人机轨迹进行决策。这些变量被分为离散型和连续型两类，分别由两个不同的强化学习智能体优化以生成组合动作。仿真结果表明，所提出的混合动作强化学习框架在不同参数设置下能有效提升上行语义数据收集效率，且性能优于基准场景。