用图神经网络预测无线频谱需求，提升精准度与可扩展性。
Towards Intelligent Spectrum Management: Spectrum Demand Estimation Using Graph Neural Networks
- 构建基于公开部署数据的频谱需求代理，利用分层多分辨率图注意力网络建模
- 在5个加拿大城市测试中，均方根误差比最优基线降低21%，残差空间偏差更小
- 结果直观可读，适合监管机构用于频谱共享与分配决策
无线连接需求增长与频谱资源有限并存，亟需更高效的频谱管理。频谱共享是可行方案，但监管机构需要准确的方法来刻画需求动态并指导分配决策。本文基于公开部署记录构建并验证了频谱需求代理，并采用分层多分辨率图注意力网络（HR-GAT）在细粒度空间尺度上估计频谱需求。该模型捕捉邻域效应与跨尺度模式，降低空间自相关性并提升泛化能力。在五个加拿大城市上评估，相对于八种竞争基线，HR-GAT将中位数均方根误差降低约21%，并减小残差空间偏差。生成的需求地图对监管机构开放，支持无线网络中的频谱共享与分配。
原文摘要 · Abstract (English)
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.
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