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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) |
| Persistent link: | http://urn.fi/urn:nbn:fi-fe2020070146550 |
| Language: | English |
| Published: |
Institute of Electrical and Electronics Engineers,
2020
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| Publish Date: | 2020-07-01 |
| Description: |
AbstractMachine 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. see all
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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 |
| OADOI: | https://oadoi.org/10.1109/LWC.2020.3002691 |
| Type of Publication: |
A1 Journal article – refereed |
| Field of Science: |
213 Electronic, automation and communications engineering, electronics |
| Subjects: | |
| 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 319008 326301 |
| Detailed Information: |
318927 (Academy of Finland Funding decision) 319008 (Academy of Finland Funding decision) 326301 (Academy of Finland Funding decision) |
| Copyright information: |
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