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

2024-08-30

arXiv.org

AI Summary

To address the challenge of attributing AI model performance degradation in 6G networks, this paper proposes an LLM-driven Chain-of-Thought (CoT) reasoning framework. First, a large language model acts as a “teacher” to generate pedagogical reasoning chains via zero-shot prompting; subsequently, a lightweight “student” model is supervised fine-tuned on these CoT traces to autonomously diagnose degradation causes. This work pioneers a two-stage paradigm—LLM-generated CoT data followed by knowledge distillation into the student model—and introduces interpretable reasoning into the 6G AI operations and maintenance (O&M) closed loop for the first time. Evaluated on a multi-access integrated testbed combining WiFi, 5G, and LiFi, the framework achieves over 97% diagnostic accuracy for real-time 3D rendering tasks. Results demonstrate strong generalizability across 6G scenarios and validate both the efficacy of the constructed CoT dataset and the robustness of the distilled student model.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• 6G Networks
• Large Language Models
• Artificial Intelligence Inference

Innovation

• Large Language Models
• Chain of Thought Reasoning
• 6G Network Optimization