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5G Network Automation Using Local Large Language Models and Retrieval-Augmented Generation

Ahmadreza Majlesara, Ali Majlesi, Ali Mamaghani, Alireza Shokrani, Babak Hossein Khalaj

TL;DR

This work presents a privacy-preserving framework for automating private 5G network configuration using locally deployed LLMs (Llama-3 8b Q-4b) augmented with Retrieval-Augmented Generation (RAG). By performing computation on-edge and leveraging a retrieval service for context, the approach reduces data exposure and operating costs while improving command accuracy. Evaluation shows that RAG substantially boosts performance for the local model (e.g., uni-gram precision ≈68% and accuracy ≈46%), though cloud-based models still outperform in raw metrics; the study highlights the practical potential of edge AI for configurable, privacy-conscious 5G networks. The findings underscore the viability of combining local LLMs with RAG to enable secure, adaptable private networks, with future work pointing to fine-tuning strategies to close remaining gaps with larger cloud models.

Abstract

This demonstration showcases the integration of a lightweight, locally deployed Large Language Model (LLaMA-3 8b Q-4b) empowered by retrieval augmented generation (RAG) to automate 5G network management, with a strong emphasis on privacy. By running the LLM on local or edge devices ,we eliminate the need for external APIs, ensuring that sensitive data remains secure and is not transmitted over the internet. Although lightweight models may not match the performance of more complex models like GPT-4, we enhance their efficiency and accuracy through RAG. RAG retrieves relevant information from a comprehensive database, enabling the LLM to generate more precise and effective network configurations based on natural language user input. This approach not only improves the accuracy of the generated configurations but also simplifies the process of creating and configuring private networks, making it accessible to users without extensive networking or programming experience. The objective of this demonstration is to highlight the potential of combining local LLMs and RAG to deliver secure, efficient, and adaptable 5G network solutions, paving the way for a future where 5G networks are both privacy-conscious and versatile across diverse user profiles.

5G Network Automation Using Local Large Language Models and Retrieval-Augmented Generation

TL;DR

This work presents a privacy-preserving framework for automating private 5G network configuration using locally deployed LLMs (Llama-3 8b Q-4b) augmented with Retrieval-Augmented Generation (RAG). By performing computation on-edge and leveraging a retrieval service for context, the approach reduces data exposure and operating costs while improving command accuracy. Evaluation shows that RAG substantially boosts performance for the local model (e.g., uni-gram precision ≈68% and accuracy ≈46%), though cloud-based models still outperform in raw metrics; the study highlights the practical potential of edge AI for configurable, privacy-conscious 5G networks. The findings underscore the viability of combining local LLMs with RAG to enable secure, adaptable private networks, with future work pointing to fine-tuning strategies to close remaining gaps with larger cloud models.

Abstract

This demonstration showcases the integration of a lightweight, locally deployed Large Language Model (LLaMA-3 8b Q-4b) empowered by retrieval augmented generation (RAG) to automate 5G network management, with a strong emphasis on privacy. By running the LLM on local or edge devices ,we eliminate the need for external APIs, ensuring that sensitive data remains secure and is not transmitted over the internet. Although lightweight models may not match the performance of more complex models like GPT-4, we enhance their efficiency and accuracy through RAG. RAG retrieves relevant information from a comprehensive database, enabling the LLM to generate more precise and effective network configurations based on natural language user input. This approach not only improves the accuracy of the generated configurations but also simplifies the process of creating and configuring private networks, making it accessible to users without extensive networking or programming experience. The objective of this demonstration is to highlight the potential of combining local LLMs and RAG to deliver secure, efficient, and adaptable 5G network solutions, paving the way for a future where 5G networks are both privacy-conscious and versatile across diverse user profiles.

Paper Structure

This paper contains 8 sections, 1 equation, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Methodology
  3. Step 1: Command Classification
  4. Step 2: Command Generation
  5. Model Implementation
  6. Results
  7. Conclusion
  8. Future Works

Figures (3)

  • Figure 1: this illustration shows our system overview and flow of data. user instruction in natural language is given to 2 parallel pipelines to get system files and retrieve similar samples from databases and give these to the LLM to generate the commands that user requested.
  • Figure 2: An example of the classifier prompt
  • Figure 3: An example of the command generator's prompt