Reinforcement Learning Based Dynamic Resource Allocation for Massive MTC in Sliced Mobile Networks

Abstract

With the rapid development of the Internet of Things (IoT) systems, the low latency requirement of massive Machine Type Communication (mMTC) in the IoT is an urgent problem to be solved for future mobile communication networks. In this paper, we use a reasonable resource allocation strategy and set priority parameters for each slice according to the average access delay of each slice. We propose a dynamic resource allocation strategy based on Markov Decision Process (MDP) modeling of mMTC random access process and using Actor-Critic (AC) algorithm in reinforcement learning. Simulations show that the proposed resource block resource allocation algorithm can reasonably allocate resources for each mMTC access slice to ensure the Quality-of-Service (QoS) requirements of mMTC applications.