A NOMA-based Q-Learning Random Access Method for Machine Type Communications

Machine Type Communications (MTC) is a main use case of 5G and beyond wireless networks. Moreover, due to the ultra-dense nature of massive MTC networks, Random Access (RA) optimization is very challenging. A promising solution is to use machine learning methods, such as reinforcement learning, to efficiently accommodate the MTC devices in RA slots. In this sense, we propose a distributed method based on Non-Orthogonal Multiple Access (NOMA) and Q-Learning to dynamically allocate RA slots to MTC devices. Numerical results show that the proposed method can significantly improve the network throughput when compared to recent work.

Authors:
da Silva Matheus Valente, Souza Richard Demo, Alves Hirley, Abrão Taufik

Publication type:
A1 Journal article – refereed

Place of publication:

Keywords:
Internet of Things, MTC, NOMA, Q-Learning

Published:
16 June 2020

Full citation:
M. V. da Silva, R. D. Souza, H. Alves and T. Abrão, “A NOMA-Based Q-Learning Random Access Method for Machine Type Communications,” in IEEE Wireless Communications Letters, vol. 9, no. 10, pp. 1720-1724, Oct. 2020, doi: 10.1109/LWC.2020.3002691

DOI:
https://doi.org/10.1109/LWC.2020.3002691

Read the publication here:
http://urn.fi/urn:nbn:fi-fe2020070146550