On End-to-End Intelligent Automation of 6G Networks
The digital transformation of businesses and services is currently in full force, opening the world to a new set of unique challenges and opportunities. In this context, 6G promises to be the set of technologies, architectures, and paradigms that will promote the digital transformation and enable gr...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-05-01
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| Series: | Future Internet |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-5903/14/6/165 |
| _version_ | 1797487293241491456 |
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| author | Abdallah Moubayed Abdallah Shami Anwer Al-Dulaimi |
| author_facet | Abdallah Moubayed Abdallah Shami Anwer Al-Dulaimi |
| author_sort | Abdallah Moubayed |
| collection | DOAJ |
| description | The digital transformation of businesses and services is currently in full force, opening the world to a new set of unique challenges and opportunities. In this context, 6G promises to be the set of technologies, architectures, and paradigms that will promote the digital transformation and enable growth and sustainability by offering the means to interact and control the digital and virtual worlds that are decoupled from their physical location. One of the main challenges facing 6G networks is “end-to-end network automation”. This is because such networks have to deal with more complex infrastructure and a diverse set of heterogeneous services and fragmented use cases. Accordingly, this paper aims at envisioning the role of different enabling technologies towards end-to-end intelligent automated 6G networks. To this end, this paper first reviews the literature focusing on the orchestration and automation of next-generation networks by discussing in detail the challenges facing efficient and fully automated 6G networks. This includes automating both the operational and functional elements for 6G networks. Additionally, this paper defines some of the key technologies that will play a vital role in addressing the research gaps and tackling the aforementioned challenges. More specifically, it outlines how advanced data-driven paradigms such as reinforcement learning and federated learning can be incorporated into 6G networks for more dynamic, efficient, effective, and intelligent network automation and orchestration. |
| first_indexed | 2024-03-09T23:45:33Z |
| format | Article |
| id | doaj.art-2f4a6591b407495d9b1195aa0f3cb4dd |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-03-09T23:45:33Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-2f4a6591b407495d9b1195aa0f3cb4dd2023-11-23T16:43:26ZengMDPI AGFuture Internet1999-59032022-05-0114616510.3390/fi14060165On End-to-End Intelligent Automation of 6G NetworksAbdallah Moubayed0Abdallah Shami1Anwer Al-Dulaimi2Electrical & Computer Engineering Department, Western University, London, ON N6A 5B9, CanadaElectrical & Computer Engineering Department, Western University, London, ON N6A 5B9, CanadaMobile Solutions Unit, EXFO Inc., Montreal, QC H4S 0A4, CanadaThe digital transformation of businesses and services is currently in full force, opening the world to a new set of unique challenges and opportunities. In this context, 6G promises to be the set of technologies, architectures, and paradigms that will promote the digital transformation and enable growth and sustainability by offering the means to interact and control the digital and virtual worlds that are decoupled from their physical location. One of the main challenges facing 6G networks is “end-to-end network automation”. This is because such networks have to deal with more complex infrastructure and a diverse set of heterogeneous services and fragmented use cases. Accordingly, this paper aims at envisioning the role of different enabling technologies towards end-to-end intelligent automated 6G networks. To this end, this paper first reviews the literature focusing on the orchestration and automation of next-generation networks by discussing in detail the challenges facing efficient and fully automated 6G networks. This includes automating both the operational and functional elements for 6G networks. Additionally, this paper defines some of the key technologies that will play a vital role in addressing the research gaps and tackling the aforementioned challenges. More specifically, it outlines how advanced data-driven paradigms such as reinforcement learning and federated learning can be incorporated into 6G networks for more dynamic, efficient, effective, and intelligent network automation and orchestration.https://www.mdpi.com/1999-5903/14/6/1656G networksintelligent automationdata-driven opportunities |
| spellingShingle | Abdallah Moubayed Abdallah Shami Anwer Al-Dulaimi On End-to-End Intelligent Automation of 6G Networks Future Internet 6G networks intelligent automation data-driven opportunities |
| title | On End-to-End Intelligent Automation of 6G Networks |
| title_full | On End-to-End Intelligent Automation of 6G Networks |
| title_fullStr | On End-to-End Intelligent Automation of 6G Networks |
| title_full_unstemmed | On End-to-End Intelligent Automation of 6G Networks |
| title_short | On End-to-End Intelligent Automation of 6G Networks |
| title_sort | on end to end intelligent automation of 6g networks |
| topic | 6G networks intelligent automation data-driven opportunities |
| url | https://www.mdpi.com/1999-5903/14/6/165 |
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