A detailed reinforcement learning framework for resource allocation in non‐orthogonal multiple access enabled‐B5G/6G networks
Abstract The world of communications technology has recently undergone an extremely significant revolution. This revolution is an immediate consequence of the immersion that the fifth generation B5G and 6G have just brought. The latter responds to the growing need for connectivity and it improves th...
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| Format: | Article |
| Language: | English |
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Wiley
2024-09-01
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| Series: | IET Networks |
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| Online Access: | https://doi.org/10.1049/ntw2.12131 |
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| author | Nouri Omheni Anis Amiri Faouzi Zarai |
| author_facet | Nouri Omheni Anis Amiri Faouzi Zarai |
| author_sort | Nouri Omheni |
| collection | DOAJ |
| description | Abstract The world of communications technology has recently undergone an extremely significant revolution. This revolution is an immediate consequence of the immersion that the fifth generation B5G and 6G have just brought. The latter responds to the growing need for connectivity and it improves the speeds and qualities of the mobile connection. To improve the energy and spectral efficiency of these types of networks, the non‐orthogonal multiple access (NOMA) technique is seen as the key solution that can accommodate more users and dramatically improve spectrum efficiency. The basic idea of NOMA is to achieve multiple access in the power sector and decode the required signal via continuous interference cancelation. A resource allocation approach is proposed for the B5G/6G‐NOMA network that aims to maximise system throughput, spectrum and energy efficiency and fairness among users while minimising latency. The proposed approach is based on reinforcement learning (RL) with the use of the Q‐Learning algorithm. First, the process of resource allocation as a problem of maximising rewards is formulated. Next, the Q‐Learning algorithm is used to design a resource allocation algorithm based on RL. The results of the simulation confirm that the proposed scheme is feasible and efficient. |
| format | Article |
| id | doaj-art-9b6db666055b4e7b95a5825880956dd2 |
| institution | OA Journals |
| issn | 2047-4954 2047-4962 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Networks |
| spelling | doaj-art-9b6db666055b4e7b95a5825880956dd22025-08-20T02:32:46ZengWileyIET Networks2047-49542047-49622024-09-01135-645547010.1049/ntw2.12131A detailed reinforcement learning framework for resource allocation in non‐orthogonal multiple access enabled‐B5G/6G networksNouri Omheni0Anis Amiri1Faouzi Zarai2NTS’Com Research Unit National School of Electronics and Telecommunications of Sfax University of Sfax Sfax TunisiaNTS’Com Research Unit National School of Electronics and Telecommunications of Sfax University of Sfax Sfax TunisiaNTS’Com Research Unit National School of Electronics and Telecommunications of Sfax University of Sfax Sfax TunisiaAbstract The world of communications technology has recently undergone an extremely significant revolution. This revolution is an immediate consequence of the immersion that the fifth generation B5G and 6G have just brought. The latter responds to the growing need for connectivity and it improves the speeds and qualities of the mobile connection. To improve the energy and spectral efficiency of these types of networks, the non‐orthogonal multiple access (NOMA) technique is seen as the key solution that can accommodate more users and dramatically improve spectrum efficiency. The basic idea of NOMA is to achieve multiple access in the power sector and decode the required signal via continuous interference cancelation. A resource allocation approach is proposed for the B5G/6G‐NOMA network that aims to maximise system throughput, spectrum and energy efficiency and fairness among users while minimising latency. The proposed approach is based on reinforcement learning (RL) with the use of the Q‐Learning algorithm. First, the process of resource allocation as a problem of maximising rewards is formulated. Next, the Q‐Learning algorithm is used to design a resource allocation algorithm based on RL. The results of the simulation confirm that the proposed scheme is feasible and efficient.https://doi.org/10.1049/ntw2.121315G mobile communicationintelligent controlresource allocation |
| spellingShingle | Nouri Omheni Anis Amiri Faouzi Zarai A detailed reinforcement learning framework for resource allocation in non‐orthogonal multiple access enabled‐B5G/6G networks IET Networks 5G mobile communication intelligent control resource allocation |
| title | A detailed reinforcement learning framework for resource allocation in non‐orthogonal multiple access enabled‐B5G/6G networks |
| title_full | A detailed reinforcement learning framework for resource allocation in non‐orthogonal multiple access enabled‐B5G/6G networks |
| title_fullStr | A detailed reinforcement learning framework for resource allocation in non‐orthogonal multiple access enabled‐B5G/6G networks |
| title_full_unstemmed | A detailed reinforcement learning framework for resource allocation in non‐orthogonal multiple access enabled‐B5G/6G networks |
| title_short | A detailed reinforcement learning framework for resource allocation in non‐orthogonal multiple access enabled‐B5G/6G networks |
| title_sort | detailed reinforcement learning framework for resource allocation in non orthogonal multiple access enabled b5g 6g networks |
| topic | 5G mobile communication intelligent control resource allocation |
| url | https://doi.org/10.1049/ntw2.12131 |
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