数据驱动的频谱需求预测：一种结合迁移学习的时空框架
Data-Driven Spectrum Demand Prediction: A Spatio-Temporal Framework with Transfer Learning
AI总结：
本文提出一种结合迁移学习的时空预测框架，利用众包KPI和监管数据，实现高精度、跨区域泛化的频谱需求预测，优于传统ITU模型。
摘要：
准确的频谱需求预测对于明智的频谱分配、有效的监管规划以及促进现代无线通信网络的可持续增长至关重要。它支持政府努力，特别是国际电信联盟（ITU）领导的努力，以建立公平的频谱分配政策、改进拍卖机制，并满足新兴技术（如高级5G、即将到来的6G和物联网（IoT））的需求。本文提出了一种有效的时空预测框架，利用众包的用户侧关键性能指标（KPIs）和监管数据集来建模和预测频谱需求。所提出的方法通过结合先进的特征工程、全面的相关性分析和迁移学习技术，实现了优越的预测精度和跨区域的泛化能力。与传统的ITU模型（通常受任意输入和不切实际的假设限制）不同，该方法利用细粒度的数据驱动洞察来解释频谱利用中的时空变化。以ITU估计为基准的比较评估强调了我们的框架能够提供更现实和可操作的预测。实验结果验证了我们方法的有效性，突出了其作为政策制定者和监管机构增强频谱管理和规划的稳健方法的潜力。
英文摘要：
Accurate spectrum demand prediction is crucial for informed spectrum allocation, effective regulatory planning, and fostering sustainable growth in modern wireless communication networks. It supports governmental efforts, particularly those led by the international telecommunication union (ITU), to establish fair spectrum allocation policies, improve auction mechanisms, and meet the requirements of emerging technologies such as advanced 5G, forthcoming 6G, and the internet of things (IoT). This paper presents an effective spatio-temporal prediction framework that leverages crowdsourced user-side key performance indicators (KPIs) and regulatory datasets to model and forecast spectrum demand. The proposed methodology achieves superior prediction accuracy and cross-regional generalizability by incorporating advanced feature engineering, comprehensive correlation analysis, and transfer learning techniques. Unlike traditional ITU models, which are often constrained by arbitrary inputs and unrealistic assumptions, this approach exploits granular, data-driven insights to account for spatial and temporal variations in spectrum utilization. Comparative evaluations against ITU estimates, as the benchmark, underscore our framework's capability to deliver more realistic and actionable predictions. Experimental results validate the efficacy of our methodology, highlighting its potential as a robust approach for policymakers and regulatory bodies to enhance spectrum management and planning.
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