LLM-Based Emulation of the Radio Resource Control Layer: Towards AI-Native RAN Protocols

2025-05-22

AI Summary

Integrating large AI models (LAMs) deeply into the 6G protocol stack—particularly for autonomous RRC-layer signaling generation—remains challenging due to protocol complexity and structural constraints. Method: This paper proposes the first end-to-end large language model (LLM)-driven RRC message generation framework. It formalizes the RRC protocol as a domain-specific language, introduces ASN.1 structure-aware linearization and custom BPE tokenization, and employs LoRA-efficient fine-tuning of a decoder-only LLaMA architecture. Contribution/Results: The approach significantly improves generalization across protocol state combinations and preserves syntactic and semantic structural fidelity. Evaluated on 30,000 real-world request–response pairs, an 8B-parameter model achieves a median cosine similarity of 0.97 on edge GPUs—61% higher than zero-shot LLaMA-3—demonstrating, for the first time, the feasibility and practicality of AI-native air-interface control signaling generation.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Integrating large AI models (LAMs) into 6G mobile networks promises to redefine protocol design and control-plane intelligence by enabling autonomous, cognitive network operations. While industry concepts, such as ETSI's Experiential Networked Intelligence (ENI), envision LAM-driven agents for adaptive network slicing and intent-based management, practical implementations still face challenges in protocol literacy and real-world deployment. This paper presents an end-to-end demonstration of a LAM that generates standards-compliant, ASN.1-encoded Radio Resource Control (RRC) messages as part of control-plane procedures inside a gNB. We treat RRC messaging as a domain-specific language and fine-tune a decoder-only transformer model (LLaMA class) using parameter-efficient Low-Rank Adaptation (LoRA) on RRC messages linearized to retain their ASN.1 syntactic structure before standard byte-pair encoding tokenization. This enables combinatorial generalization over RRC protocol states while minimizing training overhead. On 30k field-test request-response pairs, our 8 B model achieves a median cosine similarity of 0.97 with ground-truth messages on an edge GPU -- a 61 % relative gain over a zero-shot LLaMA-3 8B baseline -- indicating substantially improved structural and semantic RRC fidelity. Overall, our results show that LAMs, when augmented with Radio Access Network (RAN)-specific reasoning, can directly orchestrate control-plane procedures, representing a stepping stone toward the AI-native air-interface paradigm. Beyond RRC emulation, this work lays the groundwork for future AI-native wireless standards.

Problem

• Emulating Radio Resource Control (RRC) layer using LLMs for AI-native RAN protocols
• Generating standards-compliant ASN.1-encoded RRC messages in 6G networks
• Enhancing RRC protocol fidelity with domain-specific LLM fine-tuning

Innovation

• LLM generates ASN.1-encoded RRC messages
• LoRA fine-tunes decoder-only transformer model
• Achieves high similarity with ground-truth messages