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.

翻译：无线连接需求的激增与频谱资源的有限性，共同推动着高效频谱管理方法的发展。频谱共享是一条前景广阔的途径，但其需要对频谱需求进行精确表征，以支撑科学的决策制定。本文提出了HR-GAT，一种分层分辨率图注意力网络模型，旨在利用地理空间数据预测频谱需求。HR-GAT能够有效处理复杂的空间需求模式，并解决通常困扰标准机器学习模型的空间自相关问题——该问题常导致模型泛化能力不佳。在加拿大五个主要城市的测试中，HR-GAT相比八个基线模型将频谱需求的预测精度提升了21%，彰显了其卓越的性能与可靠性。