用图神经网络预测无线频谱需求，提升21%准确率
A Graph-Based Approach to Spectrum Demand Prediction Using Hierarchical Attention Networks
- 构建分层图注意力网络，融合地理空间数据建模
- 在5个加拿大城市测试，比8种基线模型高21%准确率
- 适合频谱管理、智能城市规划的研究者与工程师
无线连接需求激增与频谱资源有限性并存，推动高效频谱管理的发展。频谱共享虽具前景，但需精准刻画频谱需求以支持政策制定。本文提出HR-GAT——一种基于分层分辨率的图注意力网络模型，利用地理空间数据预测频谱需求。该模型有效处理复杂的空间需求模式，缓解传统机器学习模型常面临的空间自相关问题，从而改善泛化能力。在五个主要加拿大城市进行测试，相比八种基线模型，预测准确率提升21%，凸显其优越性能与可靠性。
原文摘要 · Abstract (English)
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.
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