基于P-DQN的计算卸载和资源分配在LEO卫星边缘网络中
Sensors (Basel, Switzerland)
|December 23, 2023
概括
本研究介绍了参数化深度Q网络 (P-DQN),以优化移动边缘计算 (MEC) 中的计算卸载和资源配置,并与低地球轨道 (LEO) 卫星网络集成. P-DQN方法有效地管理动态网络条件和混合行动空间,以提高任务满意度.
科学领域:
- 卫星通信 卫星通信
- 边缘计算 边缘计算
- 网络资源管理 网络资源管理
背景情况:
- 传统的低地轨道 (LEO) 卫星网络面临容量限制,并且通常独立于地面网络.
- 将移动边缘计算 (MEC) 与LEO网络集成,可以创建一个"终端云"架构,以增强任务卸载.
- 动态的网络条件和复杂的决策对LEO卫星边缘网络构成挑战.
研究的目的:
- 开发一种在动态LEO卫星边缘网络中联合计算卸载和资源分配的方法.
- 为了应对离散连续混合动力行动空间和时间变化的网络动态的挑战.
- 在综合网络中最大限度地提高长期满足任务的数量.
主要方法:
- 建模时间变化的频道特征.
- 为三个卸载场景构建通信和计算模型.
- 为任务卸载,资源可用性和功率控制制定约束.
- 使用参数化深度Q网络 (P-DQN) 和参数化动作马尔科夫决策过程 (PAMDP) 进行实时决策.
主要成果:
- 拟议的P-DQN方法在联合计算卸载,资源分配和功率控制方面表现出有效性.
- 模拟结果显示,P-DQN方法在动态LEO卫星边缘网络中实现了最佳控制.
- P-DQN的表现优于其他用于单一行动空间 (离散或连续) 的强化学习方法.
结论:
- 在集成的LEO卫星和MEC环境中,P-DQN方法为优化任务满意度提供了一个强大的解决方案.
- 这种方法成功地处理了混合行动空间和网络动态的复杂性.
- 该研究强调了先进的强化学习技术对未来卫星边缘网络管理的潜力.
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