在NB-IoT网络中,适应性节能模式控制使用软行为体关键强化学习来实现最佳的电源管理
1Department of Communication Engineering, School of Electronics Engineering, Vellore Institute of Technology, Katpadi, Vellore, Tamilnadu, 632014, India.
Scientific reports
|October 3, 2025
概括
本研究使用软行为体-关键 (SAC) 强化学习来提高窄带物联网 (NB-IoT) 网络的功率效率. 在可扩展的物联网部署中,SAC显著改善了节能和网络性能.
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:
- 物联网 (IoT) 设备需要优化功率效率,以长时间运行.
- 窄带物联网 (NB-IoT) 网络在平衡性能与节能方面面临着挑战.
研究的目的:
- 调查软行为体-关键 (SAC) 强化学习算法的有效性,用于NB-IoT设备中的电源管理.
- 将SAC的性能与近接政策优化 (PPO) 和深度Q网络 (DQN) 算法进行比较.
主要方法:
- 一个NB-IoT环境的模拟.
- 实施和比较SAC,PPO和DQN算法用于节能模式管理.
- 使用指标进行评估:总奖励,能源效率,功耗,模式计数/持续时间和工作周期.
主要成果:
- 基于SAC的方法表明,在功率效率方面取得了卓越的改进.
- 在增强的节能和保持网络性能之间实现了平衡.
- 在关键绩效指标中表现优于PPO和DQN.
结论:
- 强化学习,特别是SAC,对于推进NB-IoT的效率和可持续性至关重要.
- SAC促进了设备的长时间运行,降低了成本,提高了整体性能.
- 这种方法支持更有弹性和可扩展的物联网部署.
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