In the diverse landscape of 6G networks, where wireless connectivity demands surge and spectrum resources remain limited, flexible spectrum access becomes paramount. The success of crafting such schemes hinges on our ability to accurately characterize spectrum demand patterns across space and time. This paper presents a data-driven methodology for estimating spectrum demand variations over space and identifying key drivers of these variations in the mobile broadband landscape. By leveraging geospatial analytics and machine learning, the methodology is applied to a case study in Canada to estimate spectrum demand dynamics in urban regions. Our proposed model captures 70\% of the variability in spectrum demand when trained on one urban area and tested on another. These insights empower regulators to navigate the complexities of 6G networks and devise effective policies to meet future network demands.

翻译：在6G网络多样化的应用场景中，无线连接需求激增而频谱资源依然有限，灵活频谱接入变得至关重要。制定此类方案的成功与否，取决于我们能否准确刻画频谱需求在空间和时间上的分布模式。本文提出一种数据驱动的方法，用于估计移动宽带场景中频谱需求的空间变化并识别其关键驱动因素。通过结合地理空间分析与机器学习，该方法应用于加拿大的案例研究，以估计城市区域的频谱需求动态。我们提出的模型在一个城市区域训练并在另一个城市区域测试时，能够捕捉频谱需求70%的变异性。这些洞察有助于监管机构应对6G网络的复杂性，并制定有效政策以满足未来网络需求。