Decentralized Grant-Free mMTC Traffic Multiplexing With eMBB Data Through Deep Reinforcement Learning

Abstract

This paper addresses the problem of joint multiplexing of enhanced Mobile Broadband (eMBB) and massive Machine-Type Communications (mMTC) traffic in the same uplink time-frequency RG. Given the challenge posed by a potentially large number of users, it is essential to focus on a multiple access strategy that leverages artificial intelligence to adapt to specific channel conditions. An mMTC agent is developed through a Deep Reinforcement Learning (DRL) methodology for generating grant-free frequency hopping traffic in a decentralized manner, assuming the presence of underlying eMBB traffic dynamics. Within this DRL framework, a methodical comparison between two possible deep neural networks is conducted, using different generative models employed to ascertain their intrinsic capabilities in various application scenarios. The analysis conducted reveals that the Long Short-Term Memory network is particularly suitable for the required task, demonstrating a robustness that is consistently very close to potential upper-bounds, despite the latter requiring complete knowledge of the underlying statistics.