用数据驱动方法分析城市频谱需求变化，助力6G频谱政策制定。
Towards Flexible Spectrum Access: Data-Driven Insights into Spectrum Demand
- 基于地理空间分析与机器学习建模频谱需求动态
- 跨区域测试下捕捉70%的频谱需求变异
- 适合频谱监管者和6G网络规划人员参考
在6G网络多样化的背景下，无线连接需求激增而频谱资源有限，灵活频谱接入成为关键。成功设计此类方案依赖于对时空维度频谱需求模式的准确刻画。本文提出一种数据驱动方法，用于估算移动宽带场景中频谱需求的时空变化，并识别其主要驱动因素。通过结合地理空间分析与机器学习技术，该方法应用于加拿大的案例研究，以估算城市区域的频谱需求动态。模型在某一城市区域训练后，在另一城市区域测试时，可捕捉70%的频谱需求变异。这些洞察有助于监管机构应对6G网络复杂性，制定有效政策以满足未来网络需求。
原文摘要 · Abstract (English)
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.
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