From classical to quantum machine learning: survey on routing optimization in 6G software defined networking
The sixth generation (6G) of mobile networks will adopt on-demand self-reconfiguration to fulfill simultaneously stringent key performance indicators and overall optimization of usage of network resources. Such dynamic and flexible network management is made possible by Software Defined Networking (...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-11-01
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| Series: | Frontiers in Communications and Networks |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/frcmn.2023.1220227/full |
| _version_ | 1797492547126296576 |
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| author | Oumayma Bouchmal Bruno Cimoli Ripalta Stabile Juan Jose Vegas Olmos Idelfonso Tafur Monroy |
| author_facet | Oumayma Bouchmal Bruno Cimoli Ripalta Stabile Juan Jose Vegas Olmos Idelfonso Tafur Monroy |
| author_sort | Oumayma Bouchmal |
| collection | DOAJ |
| description | The sixth generation (6G) of mobile networks will adopt on-demand self-reconfiguration to fulfill simultaneously stringent key performance indicators and overall optimization of usage of network resources. Such dynamic and flexible network management is made possible by Software Defined Networking (SDN) with a global view of the network, centralized control, and adaptable forwarding rules. Because of the complexity of 6G networks, Artificial Intelligence and its integration with SDN and Quantum Computing are considered prospective solutions to hard problems such as optimized routing in highly dynamic and complex networks. The main contribution of this survey is to present an in-depth study and analysis of recent research on the application of Reinforcement Learning (RL), Deep Reinforcement Learning (DRL), and Quantum Machine Learning (QML) techniques to address SDN routing challenges in 6G networks. Furthermore, the paper identifies and discusses open research questions in this domain. In summary, we conclude that there is a significant shift toward employing RL/DRL-based routing strategies in SDN networks, particularly over the past 3 years. Moreover, there is a huge interest in integrating QML techniques to tackle the complexity of routing in 6G networks. However, considerable work remains to be done in both approaches in order to accomplish thorough comparisons and synergies among various approaches and conduct meaningful evaluations using open datasets and different topologies. |
| first_indexed | 2024-03-10T01:04:20Z |
| format | Article |
| id | doaj.art-3791a2242a5246e1833757b81c37d913 |
| institution | Directory Open Access Journal |
| issn | 2673-530X |
| language | English |
| last_indexed | 2024-03-10T01:04:20Z |
| publishDate | 2023-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Communications and Networks |
| spelling | doaj.art-3791a2242a5246e1833757b81c37d9132023-11-23T14:28:17ZengFrontiers Media S.A.Frontiers in Communications and Networks2673-530X2023-11-01410.3389/frcmn.2023.12202271220227From classical to quantum machine learning: survey on routing optimization in 6G software defined networkingOumayma Bouchmal0Bruno Cimoli1Ripalta Stabile2Juan Jose Vegas Olmos3Idelfonso Tafur Monroy4Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, NetherlandsDepartment of Electrical Engineering, Eindhoven University of Technology, Eindhoven, NetherlandsDepartment of Electrical Engineering, Eindhoven University of Technology, Eindhoven, NetherlandsSoftware Architecture, NVIDIA Corporation, Yokneam, IsraelDepartment of Electrical Engineering, Eindhoven University of Technology, Eindhoven, NetherlandsThe sixth generation (6G) of mobile networks will adopt on-demand self-reconfiguration to fulfill simultaneously stringent key performance indicators and overall optimization of usage of network resources. Such dynamic and flexible network management is made possible by Software Defined Networking (SDN) with a global view of the network, centralized control, and adaptable forwarding rules. Because of the complexity of 6G networks, Artificial Intelligence and its integration with SDN and Quantum Computing are considered prospective solutions to hard problems such as optimized routing in highly dynamic and complex networks. The main contribution of this survey is to present an in-depth study and analysis of recent research on the application of Reinforcement Learning (RL), Deep Reinforcement Learning (DRL), and Quantum Machine Learning (QML) techniques to address SDN routing challenges in 6G networks. Furthermore, the paper identifies and discusses open research questions in this domain. In summary, we conclude that there is a significant shift toward employing RL/DRL-based routing strategies in SDN networks, particularly over the past 3 years. Moreover, there is a huge interest in integrating QML techniques to tackle the complexity of routing in 6G networks. However, considerable work remains to be done in both approaches in order to accomplish thorough comparisons and synergies among various approaches and conduct meaningful evaluations using open datasets and different topologies.https://www.frontiersin.org/articles/10.3389/frcmn.2023.1220227/full6GroutingSDNquantum computingquantum machine learningreinforcement learning |
| spellingShingle | Oumayma Bouchmal Bruno Cimoli Ripalta Stabile Juan Jose Vegas Olmos Idelfonso Tafur Monroy From classical to quantum machine learning: survey on routing optimization in 6G software defined networking Frontiers in Communications and Networks 6G routing SDN quantum computing quantum machine learning reinforcement learning |
| title | From classical to quantum machine learning: survey on routing optimization in 6G software defined networking |
| title_full | From classical to quantum machine learning: survey on routing optimization in 6G software defined networking |
| title_fullStr | From classical to quantum machine learning: survey on routing optimization in 6G software defined networking |
| title_full_unstemmed | From classical to quantum machine learning: survey on routing optimization in 6G software defined networking |
| title_short | From classical to quantum machine learning: survey on routing optimization in 6G software defined networking |
| title_sort | from classical to quantum machine learning survey on routing optimization in 6g software defined networking |
| topic | 6G routing SDN quantum computing quantum machine learning reinforcement learning |
| url | https://www.frontiersin.org/articles/10.3389/frcmn.2023.1220227/full |
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