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Towards Intelligent Spectrum Management: Spectrum Demand Estimation Using Graph Neural Networks

Mohamad Alkadamani, Amir Ghasemi, Halim Yanikomeroglu

TL;DR

A spectrum demand proxy is built and validates from public deployment records and a graph attention network in a hierarchical, multi-resolution setup (HR-GAT) is used to estimate spectrum demand at fine spatial scales, reducing spatial autocorrelation and improving generalization.

Abstract

The growing demand for wireless connectivity, combined with limited spectrum resources, calls for more efficient spectrum management. Spectrum sharing is a promising approach; however, regulators need accurate methods to characterize demand dynamics and guide allocation decisions. This paper builds and validates a spectrum demand proxy from public deployment records and uses a graph attention network in a hierarchical, multi-resolution setup (HR-GAT) to estimate spectrum demand at fine spatial scales. The model captures both neighborhood effects and cross-scale patterns, reducing spatial autocorrelation and improving generalization. Evaluated across five Canadian cities and against eight competitive baselines, HR-GAT reduces median RMSE by roughly 21% relative to the best alternative and lowers residual spatial bias. The resulting demand maps are regulator-accessible and support spectrum sharing and spectrum allocation in wireless networks.

Towards Intelligent Spectrum Management: Spectrum Demand Estimation Using Graph Neural Networks

TL;DR

A spectrum demand proxy is built and validates from public deployment records and a graph attention network in a hierarchical, multi-resolution setup (HR-GAT) is used to estimate spectrum demand at fine spatial scales, reducing spatial autocorrelation and improving generalization.

Abstract

The growing demand for wireless connectivity, combined with limited spectrum resources, calls for more efficient spectrum management. Spectrum sharing is a promising approach; however, regulators need accurate methods to characterize demand dynamics and guide allocation decisions. This paper builds and validates a spectrum demand proxy from public deployment records and uses a graph attention network in a hierarchical, multi-resolution setup (HR-GAT) to estimate spectrum demand at fine spatial scales. The model captures both neighborhood effects and cross-scale patterns, reducing spatial autocorrelation and improving generalization. Evaluated across five Canadian cities and against eight competitive baselines, HR-GAT reduces median RMSE by roughly 21% relative to the best alternative and lowers residual spatial bias. The resulting demand maps are regulator-accessible and support spectrum sharing and spectrum allocation in wireless networks.
Paper Structure (27 sections, 19 equations, 10 figures, 4 tables, 1 algorithm)

This paper contains 27 sections, 19 equations, 10 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Traditional Approaches
  4. ML Approaches
  5. Graph Neural Networks (GNNs) for Spatial Modeling
  6. Problem Formulation
  7. Methodology
  8. Proxy Development and Validation
  9. Mapping Operator Traffic to Tiles
  10. Constructing the Deployment-Based Proxy
  11. Validation via Regression
  12. Spectrum Demand Modeling
  13. Feature Geospatial Processing
  14. Graph Construction for Multi-Resolution Representation
  15. HR-GAT Model Architecture
  16. ...and 12 more sections

Figures (10)

  • Figure 1: Overview of the proposed methodology.
  • Figure 2: Geospatial processing for proxy validation: (a) LTE cell locations and estimated coverage areas; (b) aggregated busy-hour throughput per grid tile.
  • Figure 3: Deployed bandwidth heatmaps at zoom level 15 for Calgary, Montreal, GTA, Vancouver, and Ottawa.
  • Figure 4: Feature processing pipeline for geospatial demand modeling.
  • Figure 5: Visualization of HR-GAT. (a) Hierarchical graph construction across multiple zoom levels. (b) Model architecture for spectrum demand estimation.
  • ...and 5 more figures