Accurately forecasting spectrum demand is a key component for efficient spectrum resource allocation and management. With the rapid growth in demand for wireless services, mobile network operators and regulators face increasing challenges in ensuring adequate spectrum availability. This paper presents a data-driven approach leveraging artificial intelligence (AI) and machine learning (ML) to estimate and manage spectrum demand. The approach uses multiple proxies of spectrum demand, drawing from site license data and derived from crowdsourced data. These proxies are validated against real-world mobile network traffic data to ensure reliability, achieving an R$^2$ value of 0.89 for an enhanced proxy. The proposed ML models are tested and validated across five major Canadian cities, demonstrating their generalizability and robustness. These contributions assist spectrum regulators in dynamic spectrum planning, enabling better resource allocation and policy adjustments to meet future network demands.

翻译：准确预测频谱需求是实现高效频谱资源分配与管理的关键环节。随着无线服务需求的快速增长，移动网络运营商和监管机构在确保充足频谱可用性方面面临日益严峻的挑战。本文提出一种利用人工智能（AI）与机器学习（ML）的数据驱动方法进行频谱需求估算与管理。该方法采用基于站点许可数据及众包数据衍生的多重频谱需求代理指标，并通过实际移动网络流量数据进行验证以确保可靠性，其中增强型代理指标的R$^2$值达到0.89。所提出的ML模型在加拿大五大主要城市进行了测试与验证，证明了其良好的泛化能力与鲁棒性。这些研究成果有助于频谱监管机构实施动态频谱规划，通过优化资源分配和政策调整来满足未来网络需求。