A Case Study of Spectrum Analysis Using Unsupervised Machine Learning

Shared spectrum is a building block for 5G. Understanding how allocated spectrum is utilized in time and space is necessary to identify sharing opportunities. Analysis of spectrum measurements remains a largely manual process that requires significant domain knowledge. As a step toward automated analysis of spectrum measurements, this paper describes a case study where unsupervised learning is utilized to analyze wideband spectrum measurements at two different levels.