IoT-Enabled Machine Learning for an Algorithmic Spectrum Decision Process

Abstract

This paper investigates a data centric approach for future regulatory spectrum management (SM). Spectrum sensing data are collected by a spectrum environment awareness system built on a cloud-based service of Internet of Things. The data are used to characterize channel behaviors and establish a sharing predictor model which enables a set of efficient machine learning algorithms for automated spectrum sharing decision making. The performance of the decision process is evaluated, illustrating the feasibility and potential of this novel SM approach.