A memory-centric architecture is envisioned for 6G networks to create a cognitive continuum where AI agents access multi-timescale state via zero-copy observability instead of message passing.
Agentic AI for 6G: A New Paradigm for Autonomous RAN Security Compliance
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abstract
Agentic AI systems are emerging as powerful tools for automating complex, multi-step tasks across various industries. One such industry is telecommunications, where the growing complexity of next-generation radio access networks (RANs) opens up numerous opportunities for applying these systems. Securing the RAN is a key area, particularly through automating the security compliance process, as traditional methods often struggle to keep pace with evolving specifications and real-time changes. In this article, we propose a framework that leverages LLM-based AI agents integrated with a retrieval-augmented generation (RAG) pipeline to enable intelligent and autonomous enforcement of security compliance. An initial case study demonstrates how an agent can assess configuration files for compliance with O-RAN Alliance and 3GPP standards, generate explainable justifications, and propose automated remediation if needed. We also highlight key challenges such as model hallucinations and vendor inconsistencies, along with considerations like agent security, transparency, and system trust. Finally, we outline future directions, emphasizing the need for telecom-specific LLMs and standardized evaluation frameworks.
years
2026 2verdicts
UNVERDICTED 2representative citing papers
An agentic framework uses LLMs to orchestrate MoE optimization experts for throughput, fairness, and delay objectives in joint computing and networking, achieving near-optimal simulation performance.
citing papers explorer
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Bridging the Cognitive Gap: A Unified Memory Paradigm for 6G Agentic AI-RAN
A memory-centric architecture is envisioned for 6G networks to create a cognitive continuum where AI agents access multi-timescale state via zero-copy observability instead of message passing.
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Agentic AI-Based Joint Computing and Networking via Mixture of Experts and Large Language Models
An agentic framework uses LLMs to orchestrate MoE optimization experts for throughput, fairness, and delay objectives in joint computing and networking, achieving near-optimal simulation performance.