基于学习的卸载决策算法,用于移动边缘计算网络 (EHRL)
Hend Bayoumi1, Nahla B Abdel-Hamid1, Amr M T Ali-Eldin1
1Computers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt.
PloS one
|November 26, 2025
概括
本研究介绍了一种用于移动边缘计算 (MEC) 的能量采集强化学习 (EHRL) 算法. 对于能源有限的移动设备,EHRL优化了任务卸载,提高了计算性能和时间效率.
科学领域:
- 计算机科学 计算机科学
- 电气工程 电气工程
- 电信 电信服务 电信服务 电信服务
背景情况:
- 移动设备 (MD) 在执行任务时面临能源和计算资源的限制.
- 无线电力传输 (WPT) 和能源采集 (EH) 为无线网络中的能源限制提供了解决方案.
- 移动边缘计算 (MEC) 旨在通过将资源靠近网络边缘来增强计算服务.
研究的目的:
- 在动态通道和能量采集条件下,优化无线供电MEC网络中的二进制卸载决策.
- 开发一个高效的算法,解决有限的能源可用性,并最大限度地提高MD的计算性能.
- 为了最大限度地减少任务执行延迟,并在MEC环境中提高整体系统效率.
主要方法:
- 提出了一个基于强化学习的能量采集卸载决策算法 (EHRL),集成强化学习 (RL) 和深度神经网络 (DNN).
- 采用二进制卸载策略,任务在本地执行或完全卸载到边缘服务器 (ES).
- 整合了牛顿-拉普森方法,以优化能量约束下计算速度,并利用Nadam优化器进行DNN训练.
主要成果:
- EHRL算法动态优化二进制卸载决策,而不需要手动标记数据或重复复杂的优化.
- 与传统的卸载技术相比,在计算性能和时间效率方面取得了显著的收益.
- 证明了实时和最佳卸载设计的可行性,即使在快速色的无线环境中也是如此.
结论:
- 拟议的EHRL算法有效地解决了无线供电MEC网络中有限的能源和计算资源的挑战.
- EHRL为动态任务卸载提供了强大而高效的解决方案,提高了用户体验和系统性能.
- 整合RL,DNN和优化技术为未来的MEC研发提供了一个有希望的方向.
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