基于MADDPG的综合空间-空海网络计算卸载和资源分配战略研究
1College of Information Engineering, Shanghai Maritime University, Shanghai 200135, China.
Entropy (Basel, Switzerland)
|August 28, 2025
概括
这项研究引入了一种优化海上网络计算卸载的新算法. 多代理深度决定性政策梯度 (MADDPG) 方法有效降低了海上物联网 (M-IoT) 设备的系统成本.
科学领域:
- 计算机科学
- 电气工程
- 网络工程
背景情况:
- 海上物联网 (M-IoT) 网络由于复杂,动态的环境而面临计算卸载和资源分配的挑战.
- 传统的优化方法在这些集成的空间-空气-海洋网络中与非凸度和组合复杂性作斗争.
研究的目的:
- 开发一个高效的优化算法,用于计算卸载和资源分配在集成的空间-空气-海洋网络.
- 尽量降低系统总成本,平衡M-IoT设备的能耗和延迟.
主要方法:
- 提出了一种基于多代理深度决定性政策梯度 (MADDPG) 的优化算法.
- 该系统是以部分可观测的马尔科夫决策过程 (POMDP) 为模型,集成关联,功率,计算资源分配和任务分配.
- 采用了分散执行框架的集中培训,M-IoT设备和无人机作为智能代理.
主要成果:
- 提出的基于MADDPG的算法显示了快速的趋同.
- 与基线方法相比,数值模拟显示出明显的优异性.
- 该算法使系统总成本降低了15-60%.
结论:
- 基于MADDPG的方法有效地优化了综合空间-空气-海洋网络的计算卸载和资源分配.
- 该算法通过部分任务卸载成功平衡能源消耗和延迟.
- 这种方法提供了一个强大的解决方案,用于在具有挑战性的海上环境中提高M-IoT设备的性能.
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