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

TL;DR

This paper presents HR-GAT, a hierarchical graph attention network that accurately predicts spectrum demand from geospatial data, improving prediction accuracy and addressing spatial autocorrelation issues for better spectrum management.

Contribution

The paper introduces HR-GAT, a novel hierarchical graph attention network model that effectively captures complex spatial demand patterns for spectrum prediction.

Findings

HR-GAT improves prediction accuracy by 21% over baseline models.

The model effectively handles spatial autocorrelation issues.

Tested across five Canadian cities, demonstrating robustness.

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.