Data-Driven Spectrum Demand Prediction: A Spatio-Temporal Framework with Transfer Learning

TL;DR

This paper introduces a novel spatio-temporal prediction framework utilizing transfer learning and crowdsourced data to improve spectrum demand forecasting, aiding better spectrum management and policy decisions.

Contribution

It presents a data-driven, transfer learning-based approach that significantly enhances spectrum demand prediction accuracy and generalizability over traditional models.

Findings

Outperforms ITU benchmark estimates in accuracy.

Demonstrates effective cross-regional generalization.

Validates robustness with real-world datasets.

Abstract

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.