Towards AI-Native RAN: An Operator's Perspective of 6G Day 1 Standardization
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866908445986258944 |
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| author | Li, Nan Sun, Qi Wang, Lehan Xu, Xiaofei Huang, Jinri Liu, Chunhui Gao, Jing Huang, Yuhong I, Chih-Lin |
| author_facet | Li, Nan Sun, Qi Wang, Lehan Xu, Xiaofei Huang, Jinri Liu, Chunhui Gao, Jing Huang, Yuhong I, Chih-Lin |
| contents | Artificial Intelligence/Machine Learning (AI/ML) has become the most certain and prominent feature of 6G mobile networks. Unlike 5G, where AI/ML was not natively integrated but rather an add-on feature over existing architecture, 6G shall incorporate AI from the onset to address its complexity and support ubiquitous AI applications. Based on our extensive mobile network operation and standardization experience from 2G to 5G, this paper explores the design and standardization principles of AI-Native radio access networks (RAN) for 6G, with a particular focus on its critical Day 1 architecture, functionalities and capabilities. We investigate the framework of AI-Native RAN and present its three essential capabilities to shed some light on the standardization direction; namely, AI-driven RAN processing/optimization/automation, reliable AI lifecycle management (LCM), and AI-as-a-Service (AIaaS) provisioning. The standardization of AI-Native RAN, in particular the Day 1 features, including an AI-Native 6G RAN architecture, were proposed. For validation, a large-scale field trial with over 5000 5G-A base stations have been built and delivered significant improvements in average air interface latency, root cause identification, and network energy consumption with the proposed architecture and the supporting AI functions. This paper aims to provide a Day 1 framework for 6G AI-Native RAN standardization design, balancing technical innovation with practical deployment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_08403 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards AI-Native RAN: An Operator's Perspective of 6G Day 1 Standardization Li, Nan Sun, Qi Wang, Lehan Xu, Xiaofei Huang, Jinri Liu, Chunhui Gao, Jing Huang, Yuhong I, Chih-Lin Networking and Internet Architecture Artificial Intelligence Distributed, Parallel, and Cluster Computing Machine Learning Systems and Control Artificial Intelligence/Machine Learning (AI/ML) has become the most certain and prominent feature of 6G mobile networks. Unlike 5G, where AI/ML was not natively integrated but rather an add-on feature over existing architecture, 6G shall incorporate AI from the onset to address its complexity and support ubiquitous AI applications. Based on our extensive mobile network operation and standardization experience from 2G to 5G, this paper explores the design and standardization principles of AI-Native radio access networks (RAN) for 6G, with a particular focus on its critical Day 1 architecture, functionalities and capabilities. We investigate the framework of AI-Native RAN and present its three essential capabilities to shed some light on the standardization direction; namely, AI-driven RAN processing/optimization/automation, reliable AI lifecycle management (LCM), and AI-as-a-Service (AIaaS) provisioning. The standardization of AI-Native RAN, in particular the Day 1 features, including an AI-Native 6G RAN architecture, were proposed. For validation, a large-scale field trial with over 5000 5G-A base stations have been built and delivered significant improvements in average air interface latency, root cause identification, and network energy consumption with the proposed architecture and the supporting AI functions. This paper aims to provide a Day 1 framework for 6G AI-Native RAN standardization design, balancing technical innovation with practical deployment. |
| title | Towards AI-Native RAN: An Operator's Perspective of 6G Day 1 Standardization |
| topic | Networking and Internet Architecture Artificial Intelligence Distributed, Parallel, and Cluster Computing Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2507.08403 |