Integrating Large AI Models (LAMs) into 6G mobile networks is a key enabler of the AI-Native Air Interface (AI-AI), where protocol intelligence must scale beyond handcrafted logic. This paper presents, to our knowledge, the first standards-compliant emulation of the Radio Resource Control (RRC) layer using a decoder-only LAM (LLAMA-class) fine-tuned with Low-Rank Adaptation (LoRA) on a multi-vendor corpus of real-world traces spanning both 5G and 4G systems. We treat RRC as a domain-specific language and construct a segmentation-safe, question-answer (Question-and-Answer (QA)) dataset that preserves Abstract Syntax Notation (ASN.1) structure through linearization prior to Byte Pair Encoding (BPE) tokenization. The proposed approach combines parameter-efficient adaptation with schema-bounded prompting to ensure syntactic and procedural fidelity. Evaluation introduces a standards-aware triad -- ASN.1 conformance, field-level coverage analysis, and uplink-to-downlink state-machine checks -- alongside semantic similarity and latency profiling across 120 configurations. On 30k 5G request-response pairs plus an additional 4.8k QA turns from 4G sessions, our 8B model achieves a median cosine similarity of 0.97, a 61% relative gain over a zero-shot baseline, while sustaining high conformance rates. These results demonstrate that LAMs, when augmented with protocol-aware reasoning, can directly orchestrate control-plane procedures, laying the foundation for the future Artificial Intelligence (AI)-native Radio Access Network (RAN).

翻译：将大规模人工智能模型（LAMs）集成到6G移动网络是实现AI原生空口（AI-AI）的关键使能技术，其中协议智能必须超越手工设计的逻辑。本文提出了首个基于标准兼容的无线资源控制（RRC）层仿真方案，该方案采用仅解码器架构的大语言模型（LLAMA-class），通过在涵盖5G与4G系统的多厂商真实轨迹语料库上进行低秩自适应（LoRA）微调实现。我们将RRC视为领域特定语言，构建了一个分段安全的问答（QA）数据集，该数据集在字节对编码（BPE）分词前通过线性化处理保留了抽象语法标记（ASN.1）结构。所提出的方法结合了参数高效自适应与模式边界提示技术，以确保语法和流程的保真度。评估引入了一个基于标准的三元检测体系——ASN.1一致性验证、字段级覆盖分析、上下行状态机检查——同时结合语义相似度分析和120种配置的时延性能剖析。在包含3万组5G请求-响应对及额外4.8千轮4G会话问答的数据集上，我们的80亿参数模型取得了0.97的中位余弦相似度，相较于零样本基线实现了61%的相对性能提升，同时保持了高合规率。这些结果表明，当增强协议感知推理能力时，大规模人工智能模型能够直接编排控制面流程，为未来人工智能（AI）原生无线接入网（RAN）奠定基础。