Toward Data-Driven Spectrum Management: Machine Learning Techniques for Localized Spectrum Demand Estimation
Janaki Parekh · 2025
With the expansion of 5G and the development of 6G networks, local mobile spectrum demand is expected to grow significantly. In response, spectrum regulators seek to better understand current demand to ensure spectrum-related decisions maximize the socioeconomic benefits of this finite resource and continue to foster innovation within the wireless industry. This thesis presents a data-driven approach to estimate localized mobile spectrum demand for regulatory applications. A new demand proxy, derived from crowdsourced measurements and validated using proprietary traffic data, is proposed to address limitations of traditional proxies. Next, spectrum demand modeling is framed as a regression task, various classical machine learning models are explored with geospatial data used as input features, and an interpretability technique is applied to demonstrate how these models can inform regulatory decision-making. Finally, advanced deep learning models are designed to improve performance and transfer learning is leveraged to showcase their applicability across diverse regulatory scenarios.