SAC-MS:联合切片资源分配,用户协会和无人机轨迹优化与禁飞区约束
Geng Chen1, Fang Sun1, Gang Jing2
1College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao 266590, China.
Sensors (Basel, Switzerland)
|September 27, 2025
概括
这项研究优化了空间-空气-地面集成网络 (SAGINs),通过共同管理用户协会,无人机 (UAV) 轨迹,并在禁飞区约束下切割资源. 与现有方法相比,拟议的SAC-MS算法显著提高了系统效用.
科学领域:
- 网络工程 网络工程
- 无线通信无线通信
- 网络中的人工智能
背景情况:
- 太空-空-地面综合网络 (SAGIN) 面临着越来越多的用户需求和资源限制.
- 禁飞区 (NFZ) 的限制增加了网络管理和资源分配的复杂性.
- 现有的解决方案难以有效地管理SAGIN中的多种服务和连接.
研究的目的:
- 为SAGINs提出一个共同的优化方法,以解决NFZ限制.
- 通过优化用户关联,无人机轨迹和切片资源分配来最大限度地提高系统效用.
- 开发一个有效的算法来解决复杂的,非凸的优化问题.
主要方法:
- 将非凸问题分解为用户关联,无人机轨迹和切片资源分配子问题.
- 开发了代SAC-MS算法,结合了匹配游戏理论,顺序凸近似 (SCA) 和软演员-批评 (SAC) 强化学习.
- 与TD3-MS,DDPG-MS,DQN-MS和硬切片方法进行基于模拟的比较.
主要成果:
- 拟议的SAC-MS算法在提高系统效用方面表现出卓越的性能.
- SAC-MS实现了显著的改进:比TD3-MS有10.53%,比DDPG-MS有13.17%,比DQN-MS有31.25%,比硬切片有45.38%.
- 联合优化方法有效地平衡了在NFZ约束下资源分配和网络性能.
结论:
- 该SAC-MS算法提供了一个有效的解决方案,用于SAGINs与NFZ约束的联合优化.
- 这种方法通过整合先进的优化和强化学习技术,显著提高了系统效用.
- 这些发现为在复杂的操作环境中更高效,更强大的SAGIN资源管理提供了途径.
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