A survey of deep reinforcement learning application in 5G and beyond network slicing and virtualization
The 5th Generation (5G) and beyond networks are expected to offer huge throughputs, connect large number of devices, support low latency and large numbers of business services. To realize this vision, there is a need for a paradigm shift in the way cellular networks are designed, built, and maintain...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-07-01
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| Series: | Array |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590005622000133 |
| _version_ | 1811340717546536960 |
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| author | Charles Ssengonzi Okuthe P. Kogeda Thomas O. Olwal |
| author_facet | Charles Ssengonzi Okuthe P. Kogeda Thomas O. Olwal |
| author_sort | Charles Ssengonzi |
| collection | DOAJ |
| description | The 5th Generation (5G) and beyond networks are expected to offer huge throughputs, connect large number of devices, support low latency and large numbers of business services. To realize this vision, there is a need for a paradigm shift in the way cellular networks are designed, built, and maintained. Network slicing divides the physical network infrastructure into multiple virtual networks to support diverse business services, enterprise applications and use cases. Multiple services and use cases with varying architectures and quality of service requirements on such shared infrastructure complicates the network environment. Moreover, the dynamic and heterogeneous nature of 5G and beyond networks will exacerbate network management and operations complexity. Inspired by the successful application of machine learning tools in solving complex mobile network decision making problems, deep reinforcement learning (Deep RL) methods provide potential solutions to address slice lifecycle management and operation challenges in 5G and beyond networks. This paper aims to bridge the gap between Deep RL and the 5G network slicing research, by presenting a comprehensive survey of their existing research association. First, the basic concepts of Deep RL framework are presented. 5G network slicing and virtualization principles are then discussed. Thirdly, we review challenges in 5G network slicing and the current research efforts to incorporate Deep RL in addressing them. Lastly, we present open research problems and directions for future research. |
| first_indexed | 2024-04-13T18:47:06Z |
| format | Article |
| id | doaj.art-3931b4e53af449618e73e981217d5cad |
| institution | Directory Open Access Journal |
| issn | 2590-0056 |
| language | English |
| last_indexed | 2024-04-13T18:47:06Z |
| publishDate | 2022-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Array |
| spelling | doaj.art-3931b4e53af449618e73e981217d5cad2022-12-22T02:34:34ZengElsevierArray2590-00562022-07-0114100142A survey of deep reinforcement learning application in 5G and beyond network slicing and virtualizationCharles Ssengonzi0Okuthe P. Kogeda1Thomas O. Olwal2University of the Free State, Faculty of Natural and Agricultural Sciences, Department of Computer Science and Informatics, P. O. Box 339, Bloemfontein, 9300, South AfricaUniversity of the Free State, Faculty of Natural and Agricultural Sciences, Department of Computer Science and Informatics, P. O. Box 339, Bloemfontein, 9300, South AfricaTshwane University of Technology, Faculty of Engineering and the Built Environment, Department of Electrical Engineering, Pretoria, South Africa; Corresponding author.The 5th Generation (5G) and beyond networks are expected to offer huge throughputs, connect large number of devices, support low latency and large numbers of business services. To realize this vision, there is a need for a paradigm shift in the way cellular networks are designed, built, and maintained. Network slicing divides the physical network infrastructure into multiple virtual networks to support diverse business services, enterprise applications and use cases. Multiple services and use cases with varying architectures and quality of service requirements on such shared infrastructure complicates the network environment. Moreover, the dynamic and heterogeneous nature of 5G and beyond networks will exacerbate network management and operations complexity. Inspired by the successful application of machine learning tools in solving complex mobile network decision making problems, deep reinforcement learning (Deep RL) methods provide potential solutions to address slice lifecycle management and operation challenges in 5G and beyond networks. This paper aims to bridge the gap between Deep RL and the 5G network slicing research, by presenting a comprehensive survey of their existing research association. First, the basic concepts of Deep RL framework are presented. 5G network slicing and virtualization principles are then discussed. Thirdly, we review challenges in 5G network slicing and the current research efforts to incorporate Deep RL in addressing them. Lastly, we present open research problems and directions for future research.http://www.sciencedirect.com/science/article/pii/S2590005622000133Machine learningReinforcement learningDeep reinforcement learning5GMulti-domain network slicingOrchestration |
| spellingShingle | Charles Ssengonzi Okuthe P. Kogeda Thomas O. Olwal A survey of deep reinforcement learning application in 5G and beyond network slicing and virtualization Array Machine learning Reinforcement learning Deep reinforcement learning 5G Multi-domain network slicing Orchestration |
| title | A survey of deep reinforcement learning application in 5G and beyond network slicing and virtualization |
| title_full | A survey of deep reinforcement learning application in 5G and beyond network slicing and virtualization |
| title_fullStr | A survey of deep reinforcement learning application in 5G and beyond network slicing and virtualization |
| title_full_unstemmed | A survey of deep reinforcement learning application in 5G and beyond network slicing and virtualization |
| title_short | A survey of deep reinforcement learning application in 5G and beyond network slicing and virtualization |
| title_sort | survey of deep reinforcement learning application in 5g and beyond network slicing and virtualization |
| topic | Machine learning Reinforcement learning Deep reinforcement learning 5G Multi-domain network slicing Orchestration |
| url | http://www.sciencedirect.com/science/article/pii/S2590005622000133 |
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