Demo: Deep Reinforcement Learning for Resource Management in Cellular Network Slicing

Abstract

Network slicing is considered as an efficient method to satisfy the distinct requirement of diversified services by one single infrastructure in 5G network. However, owing to the cost of information gathering and processing, it's hard to swiftly allocate resources according to the changing demands of different slices. In this demo, we consider a radio access network (RAN) scenario and develop several deep reinforcement learning (DRL) algorithms which can keenly catch the varying demands of users from different slices and learn to make an intelligent decision for resource allocation. Besides, in order to implement and evaluate our algorithms efficiently, we have also implemented a platform with a modified 3GPP Release 15 base station and several on-shelf mobile terminals. Numerical analyses of the corresponding results verify the superior performance of our methods.