University of Oulu

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

A NOMA-based Q-learning random access method for machine type communications

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Author: da Silva, Matheus Valente1; Souza, Richard Demo1; Alves, Hirley2;
Organizations: 1Department of Electrical and Electronics Engineering of the Federal University of Santa Catarina, Brazil
2Centre for Wireless Communications of the University of Oulu, Finland
3Department of Electrical Engineering, University of Londrina, Brazil
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.2 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2020
Publish Date: 2020-07-01


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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Series: IEEE wireless communications letters
ISSN: 2162-2337
ISSN-E: 2162-2345
ISSN-L: 2162-2337
Volume: 9
Issue: 10
Pages: 1720 - 1724
DOI: 10.1109/LWC.2020.3002691
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work has been supported in Brazil by CNPq, project PrInt CAPESUFSC “Automation 4.0”; in Finland by Academy of Finland (Aka) 6Genesis Flagship (Gr. 318927), EE-IoT (Gr. 319008), and FIREMAN (Gr. 326301).
Academy of Finland Grant Number: 318927
Detailed Information: 318927 (Academy of Finland Funding decision)
319008 (Academy of Finland Funding decision)
326301 (Academy of Finland Funding decision)
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