dc.contributor.author |
Khumalo, Nosipho N
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dc.contributor.author |
Oyerinde, OO
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dc.contributor.author |
Mfupe, Luzango P
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dc.date.accessioned |
2021-02-17T17:43:43Z |
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dc.date.available |
2021-02-17T17:43:43Z |
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dc.date.issued |
2021-01 |
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dc.identifier.citation |
Khumalo, N.N., Oyerinde, O. & Mfupe, L. 2021. Reinforcement learning-based resource management model for fog radio access network architectures in 5G. <i>IEEE Access, vol. 9.</i> http://hdl.handle.net/10204/11780 |
en_ZA |
dc.identifier.issn |
2169-3536 |
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dc.identifier.uri |
http://hdl.handle.net/10204/11780
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dc.description.abstract |
The need to cope with the continuously growing number of connected users and the increased demand for mobile broadband services in the Internet of Things has led to the notion of introducing the fog computing paradigm in fifth generation (5G) mobile networks in the form of fog radio access network (F-RAN). The F-RAN approach emphasises bringing the computation capability to the edge of the network so as to reduce network bottlenecks and improve latency. However, despite the potential, the management of computational resources remains a challenge in F-RAN architectures. Thus, this paper aims to overcome the shortcomings of conventional approaches to computational resource allocation in F-RANs. Reinforcement learning (RL) is presented as a method for dynamic and autonomous resource allocation, and an algorithm is proposed based on Q-learning. RL has several benefits in resource allocation problems and simulations carried out show that it outperforms reactive methods. Furthermore, the results show that the proposed algorithm improves latency and thus has the potential to have a major impact in 5G applications, particularly the Internet of Things. |
en_US |
dc.format |
Fulltext |
en_US |
dc.language.iso |
en |
en_US |
dc.relation.uri |
Doi: 10.1109/ACCESS.2021.3051695 |
en_US |
dc.relation.uri |
https://ieeexplore.ieee.org/document/9323035 |
en_US |
dc.source |
IEEE Access, vol. 9 |
en_US |
dc.subject |
Fifth generation |
en_US |
dc.subject |
Fog Computing |
en_US |
dc.subject |
Internet of Things |
en_US |
dc.subject |
IoT |
en_US |
dc.subject |
Radio access network |
en_US |
dc.subject |
Reinforcement |
en_US |
dc.title |
Reinforcement learning-based resource management model for fog radio access network architectures in 5G |
en_US |
dc.type |
Article |
en_US |
dc.description.pages |
12706-12716 |
en_US |
dc.description.note |
This work is licensed under a Creative Commons Attribution 4.0 License |
en_US |
dc.description.cluster |
Next Generation Enterprises & Institutions |
en_US |
dc.description.impactarea |
Spectrum Access Mgmt Innov |
en_US |
dc.identifier.apacitation |
Khumalo, N. N., Oyerinde, O., & Mfupe, L. (2021). Reinforcement learning-based resource management model for fog radio access network architectures in 5G. <i>IEEE Access, vol. 9</i>, http://hdl.handle.net/10204/11780 |
en_ZA |
dc.identifier.chicagocitation |
Khumalo, Nosipho N, OO Oyerinde, and Luzango Mfupe "Reinforcement learning-based resource management model for fog radio access network architectures in 5G." <i>IEEE Access, vol. 9</i> (2021) http://hdl.handle.net/10204/11780 |
en_ZA |
dc.identifier.vancouvercitation |
Khumalo NN, Oyerinde O, Mfupe L. Reinforcement learning-based resource management model for fog radio access network architectures in 5G. IEEE Access, vol. 9. 2021; http://hdl.handle.net/10204/11780. |
en_ZA |
dc.identifier.ris |
TY - Article
AU - Khumalo, Nosipho N
AU - Oyerinde, OO
AU - Mfupe, Luzango
AB - The need to cope with the continuously growing number of connected users and the increased demand for mobile broadband services in the Internet of Things has led to the notion of introducing the fog computing paradigm in fifth generation (5G) mobile networks in the form of fog radio access network (F-RAN). The F-RAN approach emphasises bringing the computation capability to the edge of the network so as to reduce network bottlenecks and improve latency. However, despite the potential, the management of computational resources remains a challenge in F-RAN architectures. Thus, this paper aims to overcome the shortcomings of conventional approaches to computational resource allocation in F-RANs. Reinforcement learning (RL) is presented as a method for dynamic and autonomous resource allocation, and an algorithm is proposed based on Q-learning. RL has several benefits in resource allocation problems and simulations carried out show that it outperforms reactive methods. Furthermore, the results show that the proposed algorithm improves latency and thus has the potential to have a major impact in 5G applications, particularly the Internet of Things.
DA - 2021-01
DB - ResearchSpace
DP - CSIR
J1 - IEEE Access, vol. 9
KW - Fifth generation
KW - Fog Computing
KW - Internet of Things
KW - IoT
KW - Radio access network
KW - Reinforcement
LK - https://researchspace.csir.co.za
PY - 2021
SM - 2169-3536
T1 - Reinforcement learning-based resource management model for fog radio access network architectures in 5G
TI - Reinforcement learning-based resource management model for fog radio access network architectures in 5G
UR - http://hdl.handle.net/10204/11780
ER - |
en_ZA |
dc.identifier.worklist |
24109 |
en_US |