AI-Enabled Data-driven Intelligence for Spectrum Demand Estimation

2026-03-10

AI Summary

This study addresses the challenge of spectrum resource management amid surging wireless service demands by proposing an artificial intelligence–based approach for spectrum demand prediction. By integrating licensed base station data with crowdsourced measurement data, the authors develop and validate several high-accuracy proxy indicators for spectrum demand. Empirical evaluation across five major Canadian cities demonstrates that the enhanced proxy metrics exhibit strong cross-city generalization capability, achieving an R² of 0.89 against real-world network traffic. This significant improvement in estimation accuracy provides a robust foundation for dynamic spectrum planning and evidence-based policy formulation.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Evaluation and Analysis
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

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.

Problem

• spectrum demand estimation
• spectrum resource allocation
• wireless services
• spectrum management
• mobile network traffic

Innovation

• AI-driven spectrum estimation
• data-driven intelligence
• spectrum demand proxy
• machine learning for wireless management
• dynamic spectrum planning