在IIoT系统中为UAV-GAP下链合作NOMA进行基于深度强化学习的资源分配
Yuanyan Huang1,2, Jingjing Su1,2, Xuan Lu1,2
1Guangxi Key Laboratory of Brain-Inspired Computing and Intelligent Chips, School of Electronic and Information Engineering, Guangxi Normal University, Guilin 541004, China.
Entropy (Basel, Switzerland)
|August 28, 2025
概括
这项研究利用深度强化学习 (DRL) 在工业物联网 (IIoT) 中优化无人机通信. 拟议的DRL框架提高了动态IIoT环境的系统吞吐量和频谱效率.
科学领域:
- 无线通信
- 人工智能
- 优化情况
背景情况:
- 工业物联网 (IIoT) 系统需要强大的通信解决方案.
- 无人机地面接入点 (GAP) 的合作网络提供了灵活的部署.
- 非直角多重接入 (NOMA) 提高了频谱效率,但也带来了分配挑战.
研究的目的:
- 为IIoT中的UAV-GAP合作NOMA系统制定联合资源分配和3D轨道优化战略.
- 解决合作和非合作用户的不同服务质量 (QoS) 要求.
- 提高系统吞吐量,光谱效率和干扰管理.
主要方法:
- 一个深度强化学习 (DRL) 框架,利用软行为-批判算法进行联合优化.
- 在连续行动空间中优化用户调度,功率分配和无人机轨迹.
- 集成封闭形式的功率分配和最大重量双边匹配,以实现有效的资源管理.
主要成果:
- 通过模拟证明了系统吞吐量和光谱效率的显著提高.
- 在动态的IIoT环境中实现有效的干扰管理.
- 对拟议方案的道不确定性的稳定性得到证实.
结论:
- 无模型强化学习与传统优化的结合是适应性资源管理的可行方法.
- 拟议的基于DRL的方案有效地处理动态UAV-GAP合作通信场景.
- 这项研究为优化 IIoT 中的通信性能提供了实用解决方案.
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