A Realization of Fog-RAN Slicing via Deep Reinforcement Learning
A Realization of Fog-RAN Slicing via Deep Reinforcement Learning
复制标题
通过深度强化学习实现 Fog-RAN 切片
DOI：
10.1109/twc.2020.2965927
复制
发表时间：
2020-01
影响因子：
10.4
通讯作者：
Peng Mugen
中科院分区：
文献类型：
--
作者：
Xiang Hongyu;Yan Shi;Peng Mugen
为了以经济高效的方式满足广泛的5G使用案例，网络切片一直被倡导为关键的推动因素。与虚拟环境中的核心网分片不同，无线接入网(RAN)分片还处于起步阶段，相应的实现具有挑战性。本文研究了FOG RAN切片的实现方法，其中关注和编排了热点场景和车辆到基础设施场景的两个网络切片实例。特别是，RAN切片的框架被描述为一个联合处理内容缓存和模式选择的优化问题，其中刻画了时变的频道和未知的内容热度分布。由于用户需求的不同和资源的有限，原始优化问题的复杂性很高，这使得传统的优化方法很难直接应用。针对这一困境，提出了一种深度强化学习算法，其核心思想是在动态的通道状态和缓存状态下，云服务器对内容缓存和模式选择做出适当的决策，以最大化奖励性能。仿真结果表明，该方案在命中率和和传输率方面都有明显的改善。
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.
登录
查看更多内容
影响因子：
10.4
作者：
Jianhua Tang;Wee Peng Tay;Tony Q. S. Quek;B. Liang
通讯作者：
Jianhua Tang;Wee Peng Tay;Tony Q. S. Quek;B. Liang
影响因子：
10.6
作者：
Li, Mingyan;Chen, Cailian;Guan, Xinping
通讯作者：
Guan, Xinping
影响因子：
16.4
作者：
Ziyue Luo;Chuan Wu;Zongpeng Li;W. Zhou
通讯作者：
Ziyue Luo;Chuan Wu;Zongpeng Li;W. Zhou
影响因子：
3.9
作者：
Peng, Mugen;Zhang, Kecheng
通讯作者：
Zhang, Kecheng
影响因子：
35.6
作者：
Afolabi, Ibrahim;Taleb, Tarik;Flinck, Hannu
通讯作者：
Flinck, Hannu