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Reinforcement learning-based computation resource allocation scheme for 5G fog-radio access network

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dc.contributor.author Khumalo, Nosipho N
dc.contributor.author Oyerinde, O
dc.contributor.author Mfupe, Luzango P
dc.date.accessioned 2021-01-04T11:29:18Z
dc.date.available 2021-01-04T11:29:18Z
dc.date.issued 2020-04
dc.identifier.citation Khumalo, N.N, Oyerinde, O and Mfupe, L.P. 2020. Reinforcement learning-based computation resource allocation scheme for 5G fog-radio access network. The Fifth International Conference on Fog and Mobile Edge Computing, Paris, France, 30 June to 3 July 2020, 3pp. en_US
dc.identifier.isbn 978-1-7281-7216-3
dc.identifier.isbn 978-1-7281-7217-0
dc.identifier.uri https://ieeexplore.ieee.org/xpl/conhome/9141176/proceeding?pageNumber=3
dc.identifier.uri https://ieeexplore.ieee.org/document/9144787
dc.identifier.uri http://hdl.handle.net/10204/11699
dc.description Copyright: 2020 IEEE. This is the pre-print version of the work. For access to the published version, please access the publisher's website. en_US
dc.description.abstract Fog computing has emerged as one of the key building blocks of fifth generation mobile networks (5G) because of its ability to effectively meet the demands of real-time or latency-sensitive applications. To introduce fog in 5G, particularly in the radio access network (RAN), intermediate network devices such as remote radio heads, small cells and macro cells are equipped with virtualised storage and processing resources to constitute the fog RAN (F-RAN). However, these resources are limited and inefficient management could cause a bottleneck for F-RAN nodes. To this end, this paper focuses on developing a dynamic and autonomous computing resource allocation scheme for F-RAN considering delay requirements of users at a node. The proposed algorithm uses reinforcement learning to optimise latency, energy consumption and cost in the F-RAN. The performance and computational complexity of the proposed algorithm will be evaluated as part of a simulation and the results compared with other algorithms from existing studies with a similar objective function. en_US
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.relation.ispartofseries Worklist;23979
dc.subject Fog computing en_US
dc.subject 5G RAN en_US
dc.subject Reinforcement learning en_US
dc.subject Edge computing en_US
dc.subject Machine learning en_US
dc.title Reinforcement learning-based computation resource allocation scheme for 5G fog-radio access network en_US
dc.type Conference Presentation en_US
dc.identifier.apacitation Khumalo, N. N., Oyerinde, O., & Mfupe, L. P. (2020). Reinforcement learning-based computation resource allocation scheme for 5G fog-radio access network. IEEE. http://hdl.handle.net/10204/11699 en_ZA
dc.identifier.chicagocitation Khumalo, Nosipho N, O Oyerinde, and Luzango P Mfupe. "Reinforcement learning-based computation resource allocation scheme for 5G fog-radio access network." (2020): http://hdl.handle.net/10204/11699 en_ZA
dc.identifier.vancouvercitation Khumalo NN, Oyerinde O, Mfupe LP, Reinforcement learning-based computation resource allocation scheme for 5G fog-radio access network; IEEE; 2020. http://hdl.handle.net/10204/11699 . en_ZA
dc.identifier.ris TY - Conference Presentation AU - Khumalo, Nosipho N AU - Oyerinde, O AU - Mfupe, Luzango P AB - Fog computing has emerged as one of the key building blocks of fifth generation mobile networks (5G) because of its ability to effectively meet the demands of real-time or latency-sensitive applications. To introduce fog in 5G, particularly in the radio access network (RAN), intermediate network devices such as remote radio heads, small cells and macro cells are equipped with virtualised storage and processing resources to constitute the fog RAN (F-RAN). However, these resources are limited and inefficient management could cause a bottleneck for F-RAN nodes. To this end, this paper focuses on developing a dynamic and autonomous computing resource allocation scheme for F-RAN considering delay requirements of users at a node. The proposed algorithm uses reinforcement learning to optimise latency, energy consumption and cost in the F-RAN. The performance and computational complexity of the proposed algorithm will be evaluated as part of a simulation and the results compared with other algorithms from existing studies with a similar objective function. DA - 2020-04 DB - ResearchSpace DP - CSIR KW - Fog computing KW - 5G RAN KW - Reinforcement learning KW - Edge computing KW - Machine learning LK - https://researchspace.csir.co.za PY - 2020 SM - 978-1-7281-7216-3 SM - 978-1-7281-7217-0 T1 - Reinforcement learning-based computation resource allocation scheme for 5G fog-radio access network TI - Reinforcement learning-based computation resource allocation scheme for 5G fog-radio access network UR - http://hdl.handle.net/10204/11699 ER - en_ZA


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