用AI预测频谱需求，帮运营商和监管方科学分配无线资源。
AI-Enabled Data-driven Intelligence for Spectrum Demand Estimation
- 结合基站许可与众包数据构建频谱需求代理指标
- 在5个加拿大大城市验证，模型预测准确率达R²=0.89
- 适合频谱管理、网络规划与政策制定者参考
精准预测频谱需求是高效频谱资源配置与管理的关键。随着无线服务需求快速增长，移动运营商与监管机构面临确保频谱充足供应的挑战。本文提出一种基于人工智能（AI）与机器学习（ML）的数据驱动方法，用于估计与管理频谱需求。该方法利用多种频谱需求代理指标，涵盖基站许可证数据及众包数据衍生信息，并通过真实移动网络流量数据验证其可靠性，其中增强型代理指标的R²值达到0.89。所提出的ML模型在五个主要加拿大城市进行了测试与验证，展现出良好的泛化能力与鲁棒性。该研究为频谱监管机构提供动态频谱规划支持，有助于优化资源分配与政策调整，以应对未来网络需求。
原文摘要 · Abstract (English)
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.
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