Evolving Towards Artificial-Intelligence-Driven Sixth-Generation Mobile Networks: An End-to-End Framework, Key Technologies, and Opportunities
The incorporation of artificial intelligence (AI) into sixth-generation (6G) mobile networks is expected to revolutionize communication systems, transforming them into intelligent platforms that provide seamless connectivity and intelligent services. This paper explores the evolution of 6G architect...
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| Format: | Article |
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MDPI AG
2025-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/6/2920 |
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| author | Zexu Li Jingyi Wang Song Zhao Qingtian Wang Yue Wang |
| author_facet | Zexu Li Jingyi Wang Song Zhao Qingtian Wang Yue Wang |
| author_sort | Zexu Li |
| collection | DOAJ |
| description | The incorporation of artificial intelligence (AI) into sixth-generation (6G) mobile networks is expected to revolutionize communication systems, transforming them into intelligent platforms that provide seamless connectivity and intelligent services. This paper explores the evolution of 6G architectures, as well as the enabling technologies required to integrate AI across the cloud, core network (CN), radio access network (RAN), and terminals. It begins by examining the necessity of embedding AI into 6G networks, making it a native capability. The analysis then outlines potential evolutionary paths for the RAN architecture and proposes an end-to-end AI-driven framework. Additionally, key technologies such as cross-domain AI collaboration, native computing, and native security mechanisms are discussed. The study identifies potential use cases, including embodied intelligence, wearable devices, and generative AI, which offer valuable insights into fostering collaboration within the AI-driven ecosystem and highlight new revenue model opportunities and challenges. The paper concludes with a forward-looking perspective on the convergence of AI and 6G technology. |
| format | Article |
| id | doaj-art-c40765115fa24c05b7f69b0cefcde416 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-c40765115fa24c05b7f69b0cefcde4162025-08-20T02:11:00ZengMDPI AGApplied Sciences2076-34172025-03-01156292010.3390/app15062920Evolving Towards Artificial-Intelligence-Driven Sixth-Generation Mobile Networks: An End-to-End Framework, Key Technologies, and OpportunitiesZexu Li0Jingyi Wang1Song Zhao2Qingtian Wang3Yue Wang4China Telecom Research Institute, Beiqijia Town, Beijing 102209, ChinaChina Telecom Research Institute, Beiqijia Town, Beijing 102209, ChinaChina Telecom Research Institute, Beiqijia Town, Beijing 102209, ChinaChina Telecom Research Institute, Beiqijia Town, Beijing 102209, ChinaChina Telecom Research Institute, Beiqijia Town, Beijing 102209, ChinaThe incorporation of artificial intelligence (AI) into sixth-generation (6G) mobile networks is expected to revolutionize communication systems, transforming them into intelligent platforms that provide seamless connectivity and intelligent services. This paper explores the evolution of 6G architectures, as well as the enabling technologies required to integrate AI across the cloud, core network (CN), radio access network (RAN), and terminals. It begins by examining the necessity of embedding AI into 6G networks, making it a native capability. The analysis then outlines potential evolutionary paths for the RAN architecture and proposes an end-to-end AI-driven framework. Additionally, key technologies such as cross-domain AI collaboration, native computing, and native security mechanisms are discussed. The study identifies potential use cases, including embodied intelligence, wearable devices, and generative AI, which offer valuable insights into fostering collaboration within the AI-driven ecosystem and highlight new revenue model opportunities and challenges. The paper concludes with a forward-looking perspective on the convergence of AI and 6G technology.https://www.mdpi.com/2076-3417/15/6/29206GAI-native RANend-to-end AIcross-domain |
| spellingShingle | Zexu Li Jingyi Wang Song Zhao Qingtian Wang Yue Wang Evolving Towards Artificial-Intelligence-Driven Sixth-Generation Mobile Networks: An End-to-End Framework, Key Technologies, and Opportunities Applied Sciences 6G AI-native RAN end-to-end AI cross-domain |
| title | Evolving Towards Artificial-Intelligence-Driven Sixth-Generation Mobile Networks: An End-to-End Framework, Key Technologies, and Opportunities |
| title_full | Evolving Towards Artificial-Intelligence-Driven Sixth-Generation Mobile Networks: An End-to-End Framework, Key Technologies, and Opportunities |
| title_fullStr | Evolving Towards Artificial-Intelligence-Driven Sixth-Generation Mobile Networks: An End-to-End Framework, Key Technologies, and Opportunities |
| title_full_unstemmed | Evolving Towards Artificial-Intelligence-Driven Sixth-Generation Mobile Networks: An End-to-End Framework, Key Technologies, and Opportunities |
| title_short | Evolving Towards Artificial-Intelligence-Driven Sixth-Generation Mobile Networks: An End-to-End Framework, Key Technologies, and Opportunities |
| title_sort | evolving towards artificial intelligence driven sixth generation mobile networks an end to end framework key technologies and opportunities |
| topic | 6G AI-native RAN end-to-end AI cross-domain |
| url | https://www.mdpi.com/2076-3417/15/6/2920 |
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