Dynamic Reservation and Deep Reinforcement Learning Based Autonomous Resource Slicing for Virtualized Radio Access Networks

Abstract

The elastic reconstruction of 5G network services is expected to provide the capability of network slice orchestration to access the network on demand, guarantee service experience on demand, and construct services on demand as well as to construct basic network services with lower costs. It is challenging to have different applications served independently with a proper resource allocation mechanism according to their own requirements. In this paper, we propose a dynamic resource reservation and deep reinforcement learning-based autonomous virtual resource slicing framework for the next generation radio access network. The infrastructure provider periodically reserves the unused resource to the virtual networks based on their ratio of minimum resource requirements. Then, the virtual networks autonomously control their resource amount using deep reinforcement learning based on the average quality of service utility and resource utilization of users. With the defined framework in this paper, virtual operators can customize their own utility function and objective function based on their own requirements. The simulation results show the performances on convergence rate, resource utilization, and satisfaction of the virtual networks.