无人机系统中的联合学习语义通信:基于PPO的联合轨迹和资源配置优化
Shuang Du1,2, Yue Zhang3, Zhen Tao1
1School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
这项研究将无人机的语义通信 (SC) 与联合学习 (FL) 整合在一起,减少计算负载. 靠近政策优化 (PPO) 算法确保了公平的服务质量,最大限度地减少了能源使用和任务时间.
科学领域:
- 无线通信无线通信
- 人工智能的人工智能
- 分布式系统 分布式系统
背景情况:
- 语义通信 (SC) 通过发送语义信息 (SI) 而不是原始数据来减少数据传输,非常适合具有尺寸,重量和功率 (SWAP) 约束的无人机.
- 联合学习 (FL) 提供了一种分布式培训方法,可以将诸如语义提取 (SE) 等计算密集型任务从无人机上卸载给边缘用户.
研究的目的:
- 证明将FL集成到无人机的SC系统中的可行性.
- 提出一种基于近接政策优化 (PPO) 的新解决方案,以实现无人机辅助SC的服务公平性.
- 共同优化无人机飞行轨迹和带宽分配,以提高效率和公平性.
主要方法:
- 在无人机通信系统中开发了用于分布式语义提取的联合学习框架.
- 制定了一个关于无人机轨迹和带宽分配的联合优化问题.
- 应用近距离政策优化 (PPO) 以确保公平的服务质量 (QoS).
主要成果:
- FL框架大大降低了无人机上的计算资源消耗.
- 基于PPO的算法有效地减少了能源消耗和任务完成时间.
- 在所有边缘用户之间确保公平的 QoS.
结论:
- 联合学习是一种可行的方法,可以减少基于UAV的语义通信中的计算负担.
- 拟议的PPO算法成功地平衡了无人机辅助SC系统的效率和公平性.
- 这项工作为更高效,更公平的无人机通信网络铺平了道路.
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