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.

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

Publication type:
A1 Journal article – refereed

Place of publication:

Internet of Things, MTC, NOMA, Q-Learning

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


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