基于深度增强学习的低地球轨道卫星的智能分层接入控制
Debin Wei1, Chuanqi Guo1, Li Yang2
1Communication and Network Laboratory, Dalian University, Dalian 116622, China.
Sensors (Basel, Switzerland)
|October 28, 2023
概括
使用深度增强学习的智能分层入场控制 (IHAC) 策略优化了低地球轨道 (LEO) 卫星的资源配置. 这种方法提高了动态卫星网络的服务接收率和公平性.
科学领域:
- 卫星通信 卫星通信
- 人工智能的人工智能
- 资源管理 资源管理
背景情况:
- 低地球轨道 (LEO) 卫星面临着有限资源和动态用户需求的挑战.
- 优化资源分配和确保公平的服务接入控制对于LEO卫星网络至关重要.
研究的目的:
- 为优化LEO卫星网络的资源分配和服务接收提出一个智能分层接入控制 (IHAC) 战略.
- 提高服务录取的公平性,同时有效地分配有限的卫星频谱资源.
主要方法:
- 开发了一个IHAC策略,结合了深度决定性政策梯度 (DDPG) 和深度Q网络 (DQN) 算法.
- 实施了层次结构框架,在全球和详细的资源管理中使用上级和下级控制器.
- 整合在线决策与离线学习,以生成适应性战略.
主要成果:
- IHAC战略显著改善了绩效指标.
- 在接受的服务中平均增加了20.36%.
- 减少了17.56%的服务下降率,平均增加了17.16%的资源公平.
结论:
- 拟议的IHAC战略有效地解决了LEO卫星网络中资源分配和入场控制方面的挑战.
- 与传统方法相比,基于DRL的方法显示出更高的适应性和性能.
- 在动态的卫星环境中,IHAC确保公平的服务接收和高效的资源利用.
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