标题： 基于预训练大语言模型的多变量无线链路质量预测 显示英文标题

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

摘要： 准确且可靠的链路质量预测（LQP）对于优化网络性能、确保无线通信中的通信稳定性以及提升用户体验至关重要。 然而，由于无线链路的动态性和易丢失性，LQP面临重大挑战，这些特性受到干扰、多径效应、衰落和阻塞的影响。 在本文中，我们提出了GAT-LLM，一种新颖的多变量无线链路质量预测模型，该模型将大型语言模型（LLMs）与图注意力网络（GAT）相结合，以实现无线通信中准确且可靠的多变量LQP。 通过将LQP框架化为时间序列预测任务并适当预处理输入数据，我们利用LLMs来提高链路质量预测的准确性。 为了应对LLMs在多变量预测中的局限性，通常处理一维数据，我们集成GAT来建模不同协议层之间多个变量的相互依赖关系，从而增强模型处理复杂依赖关系的能力。 实验结果表明，GAT-LLM显著提高了链路质量预测的准确性和鲁棒性，特别是在多步预测场景中。 显示英文摘要

摘要： 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.