A Q-Learning Based Slice Admission Algorithm for Multi-Tier 5G Cellular Wireless Networks

Abstract

Network slicing enables a 5G infrastructure provider (network infrastructure owner) to create multiple separate virtual networks, each tailored at a specific performance requirement, on a common physical network.  In this context, slice admission algorithms are required to process slice requests received by the infrastructure provider.  These algorithms are tailored to admit and allocate resources to network slices in a manner that results in the optimization of a given objective.  In this paper, a Q-learning slice admission algorithm, which maximizes the infrastructure provider’s revenue, is designed.  Results show that the designed algorithm learns from its environment, which enables it to acquire knowledge about the multi-tiered cellular network, thus allowing it make optimal slice admission decisions.  The results further show that the designed algorithm has superior performance in terms of revenue achieved when compared to algorithms that admit, a) to maximize immediate rewards and b) slices in a random manner.