Reasoning AI Performance Degradation in 6G Networks with Large Language Models

TL;DR

This paper introduces a novel LLM-based reasoning approach to diagnose AI performance degradation in 6G networks, demonstrating high accuracy in real-world multi-technology scenarios and advancing AI reliability in future networks.

Contribution

It presents a new LLM-powered Chain-of-Thought method for reasoning about AI performance issues in 6G networks, with a real-world evaluation showing over 97% accuracy.

Findings

Achieved over 97% reasoning accuracy on test questions.

Validated the effectiveness of LLM-CoT in real-time 3D rendering tasks.

Highlighted potential of LLMs to improve 6G network reliability.

Abstract

The integration of Artificial Intelligence (AI) within 6G networks is poised to revolutionize connectivity, reliability, and intelligent decision-making. However, the performance of AI models in these networks is crucial, as any decline can significantly impact network efficiency and the services it supports. Understanding the root causes of performance degradation is essential for maintaining optimal network functionality. In this paper, we propose a novel approach to reason about AI model performance degradation in 6G networks using the Large Language Models (LLMs) empowered Chain-of-Thought (CoT) method. Our approach employs an LLM as a ''teacher'' model through zero-shot prompting to generate teaching CoT rationales, followed by a CoT ''student'' model that is fine-tuned by the generated teaching data for learning to reason about performance declines. The efficacy of this model is evaluated in a real-world scenario involving a real-time 3D rendering task with multi-Access Technologies (mATs) including WiFi, 5G, and LiFi for data transmission. Experimental results show that our approach achieves over 97% reasoning accuracy on the built test questions, confirming the validity of our collected dataset and the effectiveness of the LLM-CoT method. Our findings highlight the potential of LLMs in enhancing the reliability and efficiency of 6G networks, representing a significant advancement in the evolution of AI-native network infrastructures.