journal article·2025-03-07Open access

Evolving Towards Artificial-Intelligence-Driven Sixth-Generation Mobile Networks: An End-to-End Framework, Key Technologies, and Opportunities

Zexu Li, Jingyi Wang, Song Zhao, Qingtian Wang, Yue Wang

Applied Sciences· Volume 15· Issue 6

Abstract

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.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: IoT and Edge/Fog Computing · Software-Defined Networks and 5G · Molecular Communication and Nanonetworks

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

China Telecom Research Institute, Beiqijia Town, Beijing 102209, China

Funding

Nation Key R&D Program OF FUNDER

References (48)

Show source-provided references
1. International Telecommunication Union-Radiocommunication Sector (ITU-R) (2023). IMT Vision—Framework and Overall Objectives of the Future Development of IMT for 2030 and Beyond, Recommendation, ITU.
2. Low Dynamic Range for RIS-aided Bistatic Integrated Sensing and Communication10.1109/JSAC.2025.3531533 ↗
3. Bazzi, A., Bomfin, R., Mezzavilla, M., Rangan, S., Rappaport, T., and Chafii, M. (2025). Upper Mid-Band Spectrum for 6G: Vision, Opportunity and Challenges. arXiv.
4. Bidirectional Green Promotion of 6G and AI: Architecture, Solutions, and Platform10.1109/MNET.101.2100285 ↗
5. AI-RAN in 6G Networks: State-of-the-Art and Challenges10.1109/OJCOMS.2023.3343069 ↗
6. Convergence of Networking and Cloud/Edge Computing: Status, Challenges, and Opportunities10.1109/MNET.011.2000089 ↗
7. Holistic service-based architecture for space-air-ground integrated network for 5G-advanced and beyond10.23919/JCC.2022.00.015 ↗
8. (2025, February 07). 3GPP TR 28.809. Study on Enhancement of Management Data Analytics (MDA). TR, 3GPP, v17.00. Available online: https://portal.3gpp.org/desktopmodules/Specifications/SpecificationDetails.aspx?specificationId=3694#.
9. (2025, February 07). 3GPP TR 37.817.Study on enhancement for Data Collection for NR and EN-DC. TR, 3GPP, v17.00. Available online: https://portal.3gpp.org/desktopmodules/Specifications/SpecificationDetails.aspx?specificationId=3817#.
10. (2025, February 07). 3GPP TR 38.843.Study on artificial intelligence (AI)/machine learning (ML) for NR air interface. TR, 3GPP, v18.00. Available online: https://portal.3gpp.org/desktopmodules/Specifications/SpecificationDetails.aspx?specificationId=3983#.
11. (2025, February 07). 3GPP TS 28.104.Management Data Analytics (MDA). TS, 3GPP, v19.00. Available online: https://portal.3gpp.org/desktopmodules/Specifications/SpecificationDetails.aspx?specificationId=3877.
12. (2025, February 07). 3GPP TS 23.288.Architecture enhancements for 5G System (5GS) to support network data analytics services. TS, 3GPP, v19.10. Available online: https://portal.3gpp.org/desktopmodules/Specifications/SpecificationDetails.aspx?specificationId=3579.
13. Mobility Management Paradigm Shift: From Reactive to Proactive Handover using AI/ML10.1109/MNET.2024.3357108 ↗
14. Machine Learning-Based Load Balancing Algorithms in Future Heterogeneous Networks: A Survey10.1109/ACCESS.2022.3161511 ↗
15. Farzaneh, H., Malehmirchegini, L., and Bejan, A. (2021). Artificial Intelligence Evolution in Smart Buildings for Energy Efficiency. Appl. Sci., 11.10.3390/app11020763 ↗
16. Beam Management in Ultra-Dense mmWave Network via Federated Reinforcement Learning: An Intelligent and Secure Approach10.1109/TCCN.2022.3215527 ↗
17. Decentralized Learning Based Indoor Interference Mitigation for 5G-and-Beyond Systems
18. AI-Enabled Next-Generation Communication Networks: Intelligent Agent and AI Router10.1109/MWC.001.2000100 ↗
19. Artificial-intelligence-enabled intelligent 6G networks10.1109/MNET.011.2000195 ↗
20. Hardware-accelerated platforms and infrastructures for network functions: A survey of enabling technologies and research studies10.1109/ACCESS.2020.3008250 ↗
21. Survey on network virtualization hypervisors for software defined networking10.1109/COMST.2015.2489183 ↗
22. Islam, M.R., Ahmed, M.U., Barua, S., and Begum, S. (2022). A Systematic Review of Explainable Artificial Intelligence in Terms of Different Application Domains and Tasks. Appl. Sci., 12.10.3390/app12031353 ↗
23. Wireless Network Digital Twin for 6G: Generative AI as a Key Enabler10.1109/MWC.002.2300564 ↗
24. Hao, Z., Jiang, H., Jiang, S., Ren, J., and Cao, T. (2024, January 3–7). Hybrid SLM and LLM for Edge-Cloud Collaborative Inference. Proceedings of the Workshop on Edge and Mobile Foundation Models, Tokyo, Japan.10.1145/3662006.3662067 ↗
25. Oh, S., Kim, J., Park, J., Ko, S.W., Quek, T.Q.S., and Kim, S.L. (2024). Uncertainty-Aware Hybrid Inference with On-Device Small and Remote Large Language Models. arXiv.
26. Schwarzmann, S., Civelek, T.E., and Iera, A. (2024, January 21–24). Native Support of AI Applications in 6G Mobile Networks Via an Intelligent User Plane. Proceedings of the 2024 IEEE Wireless Communications and Networking Conference (WCNC), Dubai, United Arab Emirates.10.1109/WCNC57260.2024.10570691 ↗
27. Nezami, Z., Hafeez, M., Djemame, K., and Zaidi, S.A.R. (2024). Generative AI on the Edge: Architecture and Performance Evaluation. arXiv.
28. Telecom’s Artificial General Intelligence (AGI) Vision: Beyond the GenAI Frontier10.1109/MNET.2024.3425594 ↗
29. Anomaly Based Network Intrusion Detection for IoT Attacks Using Deep Learning Technique10.1016/j.compeleceng.2023.108626 ↗
30. Ji, I.H., Lee, J.H., Kang, M.J., Park, W.J., Jeon, S.H., and Seo, J.T. (2024). Artificial Intelligence-Based Anomaly Detection Technology over Encrypted Traffic: A Systematic Literature Review. Sensors, 24.10.3390/s24030898 ↗
31. McCabe, C., Mohideen, A.I.C., and Singh, R. (2024). A Blockchain-Based Authentication Mechanism for Enhanced Security. Sensors, 24.10.3390/s24175830 ↗
32. An Unsupervised Deep Learning Model for Early Network Traffic Anomaly Detection10.1109/ACCESS.2020.2973023 ↗
33. Sahay, S.K., Sharma, A., and Rathore, H. (2018, January 30–31). Evolution of Malware and Its Detection Techniques. Proceedings of the Information and Communication Technology for Sustainable Development (ICT4SD), Goa, India.
34. Advances in Quantum Cryptography10.1364/AOP.361502 ↗
35. Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F.L., Almeida, D., Altenschmidt, J., and Altman, S. (2024). GPT-4. arXiv.
36. Anthropic (2024). The Claude 3 Model Family: Opus, Sonnet, Haiku, Technical Report; Anthropic.
37. Meta AI (2024). Introducing Meta Llama 3: The Most Capable Openly Available LLM to Date, Meta AI. Technical Report.
38. Alibaba Cloud (2025, February 28). Model List. Available online: https://help.aliyun.com/zh/model-studio/getting-started/models?spm=a2c4g.11186623.help-menu-2400256.d_0_2.37fa56e5FJFc3m.
39. Tencent (2025, February 28). Tencent Hunyuan. Available online: https://cloud.tencent.com/document/product/1729/104753.
40. Volcengine (2025, February 28). Doubao. Available online: https://www.volcengine.com/docs/82379/1330310.
41. Team, G., Georgiev, P., Lei, V.I., Burnell, R., Bai, L., Gulati, A., Tanzer, G., Vincent, D., Pan, Z., and Wang, S. (2024). Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context. arXiv.
42. Betker, J., Goh, G., Jing, L., Brooks, T., Wang, J., Li, L., Ouyang, L., Zhuang, J., Lee, J., and Guo, Y. (2023, October 20). Improving Image Generation with Better Captions. Available online: https://cdn.openai.com/papers/dall-e-3.pdf.
43. Google DeepMind (2025, February 28). Pricing—Imagen AI Post-Production Workflow Solution—imagen-ai.com. Available online: https://imagen-ai.com/pricing/.
44. Radford, A., Kim, J.W., Xu, T., Brockman, G., McLeavey, C., and Sutskever, I. (2022). Robust Speech Recognition via Large-Scale Weak Supervision. arXiv.
45. OpenAI (2025, February 28). API Pricing. Available online: https://platform.openai.com/docs/pricing.
46. Guo, D., Yang, D., Zhang, H., Song, J., Zhang, R., Xu, R., Zhu, Q., Ma, S., and Wang, P. (2025). DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning. arXiv.
47. Zhou, H., Hu, C., Yuan, D., Yuan, Y., Wu, D., Liu, X., Han, Z., and Zhang, J. (IEEE Wirel. Commun. Lett., 2024). Generative AI as a Service in 6G Edge-Cloud: Generation Task Offloading by In-Context Learning, IEEE Wirel. Commun. Lett., in press.10.1109/LWC.2024.3520995 ↗
48. At the Dawn of Generative AI Era: A Tutorial-cum-Survey on New Frontiers in 6G Wireless Intelligence10.1109/OJCOMS.2024.3362271 ↗