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)
 
Avaa tiedosto
Lataukset: 

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

da Silva, Matheus Valente
Souza, Richard Demo
Alves, Hirley
Abrão, Taufik
Institute of Electrical and Electronics Engineers
16.06.2020

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

https://rightsstatements.org/vocab/InC/1.0/
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
https://rightsstatements.org/vocab/InC/1.0/
doi:https://doi.org/10.1109/LWC.2020.3002691
Näytä kaikki kuvailutiedot
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2020070146550
Tiivistelmä

Abstract

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.

Kokoelmat