Reinforcement Learning Based Resource Allocation for Network Slicing in 5G C-RAN

In network slicing enabled 5G cloud radio access networks (C-RAN), we study the network slice resource allocation to ensure the differentiated performance requirements of diversified services from mobile networks and improve the revenue of operators. Considering the characteristics of 5G CRAN architecture, we propose a network slice resource allocation framework, which is composed of an upper layer, which performs the mapping of virtual protocol stack functions; and a lower layer, which manages radio remote unit (RRU) association, subchannel and power allocation. We model a utility maximization problem based on the proposed framework. Then we proposed a reinforcement learning based two-stage network slice resource allocation algorithm, which uses the multi-agent Q-learning process to reduce the complexity of the Q-value table. Simulation results demonstrate that the proposed algorithm can improve the whole network utility while ensuring the network performance of the virtual network operators compared with the baseline schemes.