Multivariate Wireless Link Quality Prediction Based on Pre-trained Large Language Models
This work addresses the problem of optimizing network performance and ensuring communication stability for wireless network users, but it is incremental as it builds on existing methods like LLMs and GATs.
The paper tackled the problem of accurate and reliable multivariate link quality prediction in wireless communications, which is challenging due to dynamic and lossy links, by proposing GAT-LLM, a model combining Large Language Models with Graph Attention Networks, and it demonstrated significant improvements in accuracy and robustness, especially for multi-step predictions.
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.