Semantic communications which can significantly reduce spectrum consumption in wireless networks, have recently become a popular research area. When combined with wireless power transfer (WPT), semantic communications can help achieve high spectral efficiency for energy-limited devices in wireless communications. In energy-constrained and link budget-limited scenarios such as UAV networks, the integration of semantic communications and WPT enables highly energyefficient transmission mechanisms. In this paper, we investigate semantic communications in UAV-enabled WPT networks. To achieve adaptability to varying signal-to-noise ratio (SNR) and task requirements, we introduce a multi-layer hybrid bit and semantic communication framework. We adopt a semantic communication efficiency metric and aim to maximize it by jointly optimizing UAV trajectory, energy harvesting base station (EHBS) selection, user association, semantic mode selection, and energy harvesting time allocation. To address this complex longterm optimization problem, we introduce the distributional soft actor-critic (DSAC) algorithm and introduce a decision assistant to further enhance the convergence performance of DSAC. Simulation results validate the effectiveness of the proposed method and framework and demonstrate that our algorithm can achieve superior long-term optimization performance in dynamic network environments.

翻译：语义通信能显著降低无线网络中的频谱消耗，近年来成为备受关注的研究领域。当与无线供电技术结合时，语义通信有助于在能量受限设备中实现高频谱效率。在无人机网络等能量受限且链路预算有限的场景中，语义通信与无线供电技术的融合能够构建高能效传输机制。本文研究了无人机使能无线供电网络中的语义通信问题。为适应时变信噪比及任务需求的变化，我们提出了多层混合比特与语义通信框架。采用语义通信效率指标，并通过联合优化无人机轨迹、能量采集基站选择、用户关联、语义模式选择及能量采集时间分配，最大化该指标。针对这一复杂长期优化问题，引入分布式软演员-评论家算法，并设计决策辅助模块以进一步提升算法收敛性能。仿真结果验证了所提方法与框架的有效性，表明该算法能在动态网络环境下实现优越的长期优化性能。