Agentic Assistant for 6G: Turn-based Conversations for AI-RAN Hierarchical Co-Management

TL;DR

This paper introduces an agentic, turn-based conversational system for hierarchical management of AI-enabled RAN, improving real-time control and reducing operational costs in complex network environments.

Contribution

It presents a novel multi-layered framework for human-AI interaction in AI-RAN co-management, addressing hierarchical and dynamic challenges with validated performance metrics.

Findings

Average response time of 13 seconds

78% accuracy in service design and planning

89% accuracy in AI-RAN tool operation

Abstract

New generations of radio access networks (RAN), especially with native AI services are increasingly difficult for human engineers to manage in real-time. Enterprise networks are often managed locally, where expertise is scarce. Existing research has focused on creating Retrieval-Augmented Generation (RAG) LLMs that can help to plan and configure RAN and core aspects only. Co-management of RAN and edge AI is the gap, which creates hierarchical and dynamic problems that require turn-based human interactions. Here, we create an agentic network manager and turn-based conversation assistant that can understand human intent-based queries that match hierarchical problems in AI-RAN. The framework constructed consists of: (a) a user interface and evaluation dashboard, (b) an intelligence layer that interfaces with the AI-RAN, and (c) a knowledge layer for providing the basis for evaluations and recommendations. These form 3 layers of capability with the following validation performances (average response time 13s): (1) design and planning a service (78\% accuracy), (2) operating specific AI-RAN tools (89\% accuracy), and (3) tuning AI-RAN performance (67\%). These initial results indicate the universal challenges of hallucination but also fast response performance success that can really reduce OPEX costs for small scale enterprise users.