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Intelligent service-based RAN for 6G: Functional recomposition and intent-driven orchestration
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DOI：10.23919/jcc.fa.2025-0344.202604.png)
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Abstract
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As the sixth generation (6G) networks aim to support increasingly diverse, dynamic and intelligent services, conventional radio access network (RAN) architectures face challenges in flexibility, scalability and service responsiveness. In this context, the concept of service-based RAN has emerged as a promising architectural evolution to achieve multi-dimensional coordination across function, service and resource. This paper presents an intelligent service-based RAN architecture using embedded Artificial Intelligence (AI) techniques, characterized by a dual-module design that integrates functional recomposition and intent-driven orchestration. Specifically, the functional decoupling module introduces a two-stage mechanism, including semantic decoupling and function recomposition. Leveraging large language models (LLMs) for semantic parsing of heterogeneous protocol documents, atomic functions are extracted and standardized. These functions are then aggregated to form RAN services for the control plane and user plane respectively. On the orchestration side, we develop an intent-driven approach in which LLMs parse high-level service requirements and translate them into service function chains and resource mappings. Simulation results validate the effectiveness of the proposed functional recomposition and orchestration, highlighting reliable guarantee for the intent of users. Finally, several key challenges are identified that will be critical to the future evolution of intelligent service-based RAN.
Keywords:
AI
intent-driven orchestration
functional recomposition
service-based RAN
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