Multivariate Wireless Link Quality Prediction Based on Pre-trained Large Language Models

TL;DR

This paper introduces GAT-LLM, a novel model combining Large Language Models and Graph Attention Networks to improve multivariate wireless link quality prediction accuracy and robustness in dynamic environments.

Contribution

The paper presents a new multivariate LQP model that integrates LLMs with GATs, addressing the challenge of modeling complex interdependencies among multiple variables.

Findings

GAT-LLM outperforms existing models in prediction accuracy.

The model demonstrates robustness in multi-step prediction scenarios.

Experimental results show significant improvements in dynamic wireless environments.

Abstract

Accurate and reliable link quality prediction (LQP) is crucial for optimizing network performance, ensuring communication stability, and enhancing user experience in wireless communications. However, LQP faces significant challenges due to the dynamic and lossy nature of wireless links, which are influenced by interference, multipath effects, fading, and blockage. In this paper, we propose GAT-LLM, a novel multivariate wireless link quality prediction model that combines Large Language Models (LLMs) with Graph Attention Networks (GAT) to enable accurate and reliable multivariate LQP of wireless communications. By framing LQP as a time series prediction task and appropriately preprocessing the input data, we leverage LLMs to improve the accuracy of link quality prediction. To address the limitations of LLMs in multivariate prediction due to typically handling one-dimensional data, we integrate GAT to model interdependencies among multiple variables across different protocol layers, enhancing the model's ability to handle complex dependencies. Experimental results demonstrate that GAT-LLM significantly improves the accuracy and robustness of link quality prediction, particularly in multi-step prediction scenarios.