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

arXiv: https://arxiv.org/abs/2603.10802 · HTML: https://mlsanity.com/paper/2603.10802/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 36 references that are in the map.
- First posted on arXiv 11 Mar 2026.

## Builds on (most cited first, 7 of 7)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [An Introduction to Convolutional Neural Networks](https://mlsanity.com/paper/1511.08458/) (2015)
- [Graph Construction with Flexible Nodes for Traffic Demand Prediction](https://mlsanity.com/paper/2403.00276/) (2024)
- [Systematic Literature Review of AI-enabled Spectrum Management in 6G and Future Networks](https://mlsanity.com/paper/2407.10981/) (2024)
- [Towards Flexible Spectrum Access: Data-Driven Insights into Spectrum Demand](https://mlsanity.com/paper/2603.09942/) (2026)

## Abstract (arXiv)

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.