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

• URL: http://arxiv.org/abs/2505.16821v2
• Date: Sun, 25 May 2025 20:15:17 GMT
• Title: LLM-Based Emulation of the Radio Resource Control Layer: Towards AI-Native RAN Protocols
• Authors: Ziming Liu, Bryan Liu, Alvaro Valcarce, Xiaoli Chu,
• Abstract summary: Integrating large AI models into 6G mobile networks promises to redefine protocol design and control-plane intelligence.<n>This paper presents an end-to-end demonstration of a LAM that generates standards-compliant, ASN.1-encoded Radio Resource Control messages.<n>Our results show that LAMs, when augmented with Radio Access Network (RAN)-specific reasoning, can directly orchestrate control-plane procedures.
• Score: 18.837338133444423
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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