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

2025-01-20

AI Summary

Accurate prediction of wireless link quality remains challenging due to multi-source interference, time-varying propagation paths, and dynamic obstructions. To address this, we formulate multi-variable link quality prediction as a cross-layer, multi-step temporal forecasting task. We propose the first collaborative architecture integrating a large language model (LLM) and a graph attention network (GAT): the LLM captures long-range temporal dependencies for individual variables, while the GAT explicitly models cross-layer topological relationships among heterogeneous variables; heterogeneous features are fused via embedding alignment. This design overcomes inherent limitations of conventional LLMs in multidimensional time-series modeling, substantially enhancing prediction robustness and generalization. Experimental results demonstrate an average 23.6% reduction in multi-step prediction error compared to state-of-the-art baselines.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Planning, Routing, and Scheduling: Planning with Language Models
• Natural Language Processing: (Large) Language Models

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Wireless Signal Prediction
• Network Performance Optimization
• Signal Quality Variation

Innovation

• GAT-LLM
• Graph Attention Networks
• Wireless Signal Quality Prediction