Probabilistic Preamble Selection with Reinforcement Learning for Massive Machine Type Communication (MTC) Devices

The significant increase of Machine-Type Communication (MTC) devices has left cellular networks facing unprecedented challenges to support them. One of the major challenges is the Random Access (RA) process performed by idle or disconnected devices. As MTC devices can be highly synchronized, with numerous devices attempting to establish a connection at the same time, the limited available resources during RA allow only for a small number of them to connect to the network, while the rest are required to try again later, increasing their delay. In this paper, we tackle the challenge of the RA process and propose a novel algorithm based on the recent advancements of Non-Orthogonal Random Access (NORA) schemes and the addition of Reinforcement Learning (RL), that is able to increase the number of successful connections, and decrease the network access delay. Our results show that our approach significantly outperforms the currently used approaches.