Reinforcement Learning for 5G Caching with Dynamic Cost
Reinforcement Learning for 5G Caching with Dynamic Cost
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DOI：
10.1109/icassp.2018.8462673
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发表时间：
2018-04
期刊：
影响因子：
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通讯作者：
A. Sadeghi;Fatemeh Sheikholeslami;A. Marques;G. Giannakis
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文献类型：
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作者：
A. Sadeghi;Fatemeh Sheikholeslami;A. Marques;G. Giannakis
在下一代蜂窝网络(5G)中，接入点(AP)预计将配备存储设备，通过在网络边缘缓存可重复使用的流行内容来在本地服务请求。最终目标是将回程链路上的部分负载从高峰时段转移到非高峰时段，从而改善整体网络性能和服务体验。为了使AP具有能够在动态设置下工作的高效(最优)取缓存决策方案，我们引入了简单但灵活的通用时变取取和缓存成本，然后将其用于表示跨文件和时间的总成本的约束最小化。由于每个时隙的缓存决策会影响未来时刻的内容可用性，因此新的最优获取缓存决策公式属于动态规划的范畴，提出了基于强化学习的高效解算器。通过数值测试对算法的性能进行了评估，并讨论了取数与缓存之间的内在权衡。
In next generation cellular networks (5G) the access points (APs) are anticipated to be equipped with storage devices to serve locally requests for reusable popular contents by caching them at the edge of the network. The ultimate goal is to shift part of the load on the back-haul links from on-peak to off-peak periods, contributing to a better overall network performance and service experience. In order to enable the APs with efficient (optimal) fetch-cache decision making schemes able to work in dynamic settings, we introduce simple but flexible generic time-varying fetching and caching costs, which are then used to formulate a constrained minimization of the aggregate cost across files and time. Since caching decisions in every time slot influence the content availability in future instants, the novel formulation for optimal fetch-cache decisions falls into the class of dynamic programming, for which efficient reinforcement-learning-based solvers are proposed. The performance of our algorithms is assessed via numerical tests, and discussions on the inherent fetching-versus-caching trade-off are provided.