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

Mohamad Alkadamani, Halim Yanikomeroglu, Amir Ghasemi

TL;DR

HR-GAT, a hierarchical resolution graph attention network model, designed to predict spectrum demand using geospatial data, is introduced, which adeptly handles complex spatial demand patterns and resolves issues of spatial autocorrelation that usually challenge standard machine learning models.

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.

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

TL;DR

HR-GAT, a hierarchical resolution graph attention network model, designed to predict spectrum demand using geospatial data, is introduced, which adeptly handles complex spatial demand patterns and resolves issues of spatial autocorrelation that usually challenge standard machine learning models.

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.
Paper Structure (13 sections, 7 equations, 6 figures, 4 tables, 1 algorithm)

This paper contains 13 sections, 7 equations, 6 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Problem Formulation
  3. Methodology
  4. Graph Construction for Multi-Resolution Learning
  5. Feature Processing
  6. HR-GAT Model Architecture
  7. Algorithm and Experimental Setup
  8. Proxy Validation
  9. Performance Evaluation
  10. Cross-Validation Results (CBCV)
  11. Generalization to Unseen City (LOCO)
  12. Key Factors Influencing Spectrum Demand
  13. Conclusion

Figures (6)

  • Figure 1: Illustration of the HR-GAT hierarchical graph construction.
  • Figure 2: HR-GAT Architecture: Multi-Zoom Spectrum Demand Estimation Model.
  • Figure 3: Deployed bandwidth heatmaps at zoom level 15 for Calgary, Montreal, Toronto, Vancouver, and Ottawa. Darker regions indicate higher estimated spectrum deployment.
  • Figure 4: eCDF of RMSE values across models. HR-GAT demonstrates superior prediction accuracy.
  • Figure 5: Log-scaled residual error distribution across cities, showing HR-GAT's consistent performance.
  • ...and 1 more figures