AI 中文总结
针对传统RAN智能算法泛化差、通用LLM算力高且缺领域知识的问题，提出EvoRIC分层框架，结合RLFT与PPO优化LLM，在IAB网络中验证其泛化性与效能，为自主O-RAN提供新方案。
AI 中文摘要
尽管近期将人工智能（AI）技术应用于无线接入网（RAN）取得了进展，但仍存在关键挑战：传统机器学习（ML）算法在不同网络拓扑间的泛化能力有限，而通用大语言模型（LLM）面临高计算需求且缺乏领域特定知识。为解决这些差距，本文提出演进型RAN智能控制器（RIC）框架EvoRIC，该框架为分层架构，通过利用非实时RIC（non-RT RIC）进行全局模型更新、近实时RIC（near-RT RIC）进行本地执行，动态赋予LLM领域特定决策能力，实现持续演进。在该框架内，我们采用基于强化学习的微调（RLFT）机制，其中LLM作为近端策略优化（PPO）智能体中的行动者。通过利用从无线环境收集的交互元组，迭代更新LLM参数，使其语义推理与严格的网络性能目标对齐。我们在集成接入回传（IAB）网络中评估所提EvoRIC框架的泛化性和效能，最后讨论该框架实现自主O-RAN的开放挑战与未来方向。
英文摘要
Despite recent advances in applying artificial intelligence (AI) techniques to radio access network (RAN), critical challenges remain: traditional machine learning (ML) algorithms suffer from limited generalization across varying network topologies, whereas general-purpose large language models (LLMs) face high computational demands and lack domain-specific knowledge. To address these gaps, this article introduces the evolving RAN intelligent controller (RIC) (EvoRIC) framework, a hierarchical architecture that enables continuous evolution by leveraging a non-real-time RIC (non-RT RIC) for global model updates and a near-real-time RIC (near-RT RIC) for local execution, dynamically empowering LLMs with domain-specific decision-making capabilities. Within this framework, we employ a reinforcement learning-based fine-tuning (RLFT) mechanism where an LLM operates as an actor within a proximal policy optimization (PPO) agent. By leveraging the interaction tuples collected from the wireless environment, the LLM's parameters are iteratively updated to align semantic reasoning with rigorous network performance objectives. We evaluate the generalization and efficacy of the proposed EvoRIC framework within integrated access and backhaul (IAB) networks, and finally, discuss the open challenges and future directions of the EvoRIC framework toward realizing autonomous O-RAN.
CommentsManuscript submitted 23 April 2026; revised 7 August 2026
