用大模型+图注意力网络，精准预测多变量无线链路质量
Multivariate Wireless Link Quality Prediction Based on Pre-trained Large Language Models
- 将链路质量预测建模为时序任务，结合大模型与图注意力网络
- 在多步预测中显著提升准确率与鲁棒性
- 适合需要高可靠通信的无线网络场景
精确可靠的链路质量预测（LQP）对于优化无线通信网络性能、保障通信稳定性和提升用户体验至关重要。然而，由于无线链路具有动态性和易丢失性，受干扰、多径效应、衰落和遮挡等因素影响，传统方法面临挑战。本文提出GAT-LLM，一种新型多变量无线链路质量预测模型，将大语言模型（LLM）与图注意力网络（GAT）相结合，实现对无线通信中多变量链路质量的精准预测。通过将LQP建模为时序预测任务并合理预处理输入数据，利用LLM提升预测精度；针对LLM在多变量预测中通常仅处理一维数据的局限性，引入GAT以建模不同协议层间多个变量的依赖关系，增强对复杂依赖结构的捕捉能力。实验结果表明，GAT-LLM在多步预测场景下显著提升了链路质量预测的准确性与鲁棒性。
原文摘要 · Abstract (English)
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.
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