A Graph-Based Approach to Spectrum Demand Prediction Using Hierarchical Attention Networks

2026-03-10

AI Summary

This study addresses the growing tension between surging wireless connectivity demands and scarce spectrum resources by proposing a novel approach to accurate spectrum demand modeling for efficient sharing. The work introduces the Hierarchical-Resolution Graph Attention Network (HR-GAT), which, to the best of our knowledge, is the first to apply graph neural networks to spectrum demand prediction. By integrating geospatial data and employing a hierarchical modeling strategy, HR-GAT effectively mitigates spatial autocorrelation issues inherent in traditional approaches. Experimental evaluations across five major Canadian cities demonstrate that the proposed method improves prediction accuracy by 21% on average compared to eight baseline models, while significantly enhancing generalization capability and spatial modeling precision.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

The surge in wireless connectivity demand, coupled with the finite nature of spectrum resources, compels the development of efficient spectrum management approaches. Spectrum sharing presents a promising avenue, although it demands precise characterization of spectrum demand for informed policy-making. This paper introduces HR-GAT, a hierarchical resolution graph attention network model, designed to predict spectrum demand using geospatial data. HR-GAT adeptly handles complex spatial demand patterns and resolves issues of spatial autocorrelation that usually challenge standard machine learning models, often resulting in poor generalization. Tested across five major Canadian cities, HR-GAT improves predictive accuracy of spectrum demand by 21% over eight baseline models, underscoring its superior performance and reliability.

Problem

• spectrum demand prediction
• spectrum management
• spatial autocorrelation
• wireless connectivity
• spectrum sharing

Innovation

• Graph Attention Network
• Spectrum Demand Prediction
• Hierarchical Resolution
• Spatial Autocorrelation
• Geospatial Data