Towards Flexible Spectrum Access: Data-Driven Insights into Spectrum Demand

2026-03-10

AI Summary

This study addresses the critical challenge of spectrum scarcity amid surging wireless demands in 6G networks by proposing a data-driven approach that integrates geospatial analysis with machine learning to accurately characterize the spatiotemporal dynamics of spectrum demand. For the first time, this method enables cross-city modeling of spectrum requirements and effectively identifies key influencing factors. Validated across multiple cities, the framework explains over 70% of the observed variation in spectrum demand, offering regulators a highly accurate and interpretable decision-support tool for designing dynamic spectrum sharing policies tailored to 6G.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Search and Optimization: Evaluation and Analysis

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution

Abstract

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.

Problem

• spectrum demand
• flexible spectrum access
• 6G networks
• spatial-temporal variation
• wireless connectivity

Innovation

• data-driven spectrum demand
• geospatial analytics
• machine learning
• flexible spectrum access
• 6G networks