Throughput Maximization in C-RAN Enabled Virtualized Wireless Networks via Multi-Agent Deep Reinforcement Learning

With the excessive growth in mobile users’ traffic, radio resource management (RRM) techniques should undergo revolutionary changes to be competent enough to meet the ever-increasing users’ demands. Virtualized wireless network (VWN) has emerged as a satisfactory solution in the fifth-generation (5G) cellular networks ensuring the required quality-of-service (QoS) of distinct slices. Yet, it seems that tackling RRM problems in VWNs using conventional optimization is not practical for real-time applications. In this paper, driven by the advancements of machine learning, we consider the throughput maximization problem in a cloud radio access network (C-RAN) assisted softly virtualized wireless network supporting different types of services and solve it with a deep Q-learning (DQL) algorithm. The performance of the proposed policy is thoroughly evaluated via simulation results with respect to the isolation rate, penalty value as well as the discount factor. It is shown that our proposed policy achieves a higher sum rate compared to the existing baseline namely a greedy search-based power allocation strategy.