A NOMA-based Q-learning random access method for machine type communications
da Silva, Matheus Valente; Souza, Richard Demo; Alves, Hirley; Abrão, Taufik (2020-06-16)
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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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.
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