On using reinforcement learning for network slice admission control in 5G: Offline vs. online

Achieving a fair usage of network resources is of vital importance in Slice-ready 5G network. The dilemma of which network slice to accept or to reject is very challenging for the Infrastructure Provider (InfProv). On one hand, InfProv aims to maximize the network resources usage by accepting as many network slices as possible; on the other hand, the network resources are limited, and the network slice requirements regarding Quality of Service (QoS) need to be fulfilled. In this paper, we devise three admission control mechanisms based on Reinforcement Learning, namely, Q-Learning, Deep Q-Learning, and Regret Matching, which allow deriving admission control decisions (policy) to be applied by InfProv to admit or reject network slice requests. We evaluated the three algorithms using computer simulation, showing results on each mechanism's performance in terms of maximizing the InfProv revenue and their ability to learn offline or online.

Automated summary

Achieving fair usage of network resources in Slice-ready 5G network is crucial, and Infrastructure Providers face a challenging dilemma of accepting or rejecting network slices. This paper proposes three admission control mechanisms based on Reinforcement Learning, which help InfProv make decisions on network slice requests.