Reinforcement Learning Approach for Advanced Sleep Modes Management in 5G Networks
Reinforcement Learning Approach for Advanced Sleep Modes Management in 5G Networks
复制标题
5G 网络中高级睡眠模式管理的强化学习方法
DOI：
10.1109/vtcfall.2018.8690555
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
E. Altman
中科院分区：
文献类型：
--
作者：
F. Salem;Z. Altman;A. Gati;T. Chahed;E. Altman
高级睡眠模式(ASM)对应于基站(BS)的S组件的逐渐停用，以降低其能量消耗(EC)。根据每个组件的转换时间(停用和激活持续时间)，可以考虑不同级别的休眠模式(SM)。本文提出了一种基于Q-学习方法的ASM管理方案。目标是根据网络运营商在降低EC和时延约束方面的要求，找到每个SM级别的最优持续时间。提出的解决方案表明，即使对延迟有很高的限制，我们也可以在低负载场景下实现高能量节约(高达EC减少的57%)，而不会对延迟造成任何影响。当延迟限制放松时，我们可以实现高达90%的节能。
Advanced Sleep Modes (ASMs) correspond to a gradual deactivation of the Base Station (BS)'s components in order to reduce its Energy Consumption (EC). Different levels of Sleep Modes (SMs) can be considered according to the transition time (deactivation and activation durations) of each component. We propose in this paper a management solution for ASMs based on Q-learning approach. The target is to find the optimal durations for each SM level according to the requirements of the network operator in terms of EC reduction and delay constraints. The proposed solution shows that even with a high constraint on the delay, we can achieve high energy savings in a low load scenario (up to 57% of EC reduction) without inducing any impact on the delay. When the delay constraint is relaxed, we can achieve up to almost 90% of energy savings.
影响因子：
35.6
作者：
Aliu, Osianoh Glenn;Imran, Ali;Evans, Barry
通讯作者：
Evans, Barry