MARL-based Random Access Scheme for Delay-constrained umMTC in 6G

Abstract

With the development of IoT technology, 6G defines ultra-massive machine type communication (umMTC) as a core service type. Since umMTC in 6G is composed of a huge number of devices and various IoT service types, an efficient random access (RA) scheme for massive devices is required. We study a scheme that maximizes the successful RA ratio by applying multi-agent reinforcement learning (MARL) in the delay-constrained 6G umMTC environment. We define the necessary information for the optimal RA strategy and describe how to obtain the RA information with machine-type communication device (MTCD) grouping and learning framework. We utilize the QMIX learning framework to solve the non-stationarity problem in MARL and design the learning framework to select optimal RA for each MTCD group. We conduct a simulation to verify the proposed scheme and simulation results show that a successful RA ratio can be improved up to 20% compared to the state-of-the-art in non-uniform device distribution. © 2023 IEEE.