To meet the wide range of 5G use cases in a cost-efficient way, network slicing has been advocated as a key enabler. Unlike the core network slicing in a virtualized environment, radio access network (RAN) slicing is still in its infancy and the corresponding realization is challenging. In this paper, we investigate the realization approach of fog RAN slicing, where two network slice instances for hotspot and vehicle-to-infrastructure scenarios are concerned and orchestrated. In particular, the framework for RAN slicing is formulated as an optimization problem of jointly tackling content caching and mode selection, in which the time-varying channel and unknown content popularity distribution are characterized. Due to the different users’ demands and the limited resources, the complexity of original optimization problem is significant high, which makes traditional optimization approaches hard to be directly applied. To deal with this dilemma, a deep reinforcement learning algorithm is proposed, whose core idea is that the cloud server makes proper decisions on the content caching and mode selection to maximize the reward performance under the dynamical channel state and cache status. The simulation results demonstrate the performance in terms of hit ratio and sum transmit rate can be significantly improved by the proposal.

中文翻译：

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通过深度强化学习实现 Fog-RAN 切片

为了以经济高效的方式满足广泛的 5G 用例，网络切片被认为是一个关键的推动因素。与虚拟化环境中的核心网切片不同，无线接入网 (RAN) 切片仍处于起步阶段，相应的实现具有挑战性。在本文中，我们研究了雾 RAN 切片的实现方法，其中关注和编排了用于热点和车辆到基础设施场景的两个网络切片实例。特别地，RAN切片的框架被表述为联合解决内容缓存和模式选择的优化问题，其中时变信道和未知的内容流行度分布被表征。由于用户需求不同，资源有限，原始优化问题的复杂度非常高，这使得传统的优化方法难以直接应用。针对这一困境，提出了一种深度强化学习算法，其核心思想是云服务器对内容缓存和模式选择做出适当的决策，以在动态通道状态和缓存状态下最大化奖励性能。仿真结果表明，该方案可以显着提高命中率和总传输率方面的性能。