When Network Slicing meets Deep Reinforcement Learning

Abstract

5G will serve various new use cases that have diverse requirements of multiple resources, e.g., radio, transportation, and computing. Network slicing is a promising technology to slice the network according to the requirements of different use cases. In this work, we present an end-to-end network slicing system that leverages deep reinforcement learning to efficiently orchestrate multiple resources in radio access network, transportation network, and edge computing servers to network slices.