Identifying Spectrum Usage by Unknown Systems using Experiments in Machine Learning

We adopt a machine learning approach towards the problem of identifying wireless systems present in a dynamic radio environment with heterogeneous usage. To classify the wireless systems, we utilize two features that typify spectrum use-center frequency and the frequency spread-and cluster the measurement data in this space. Since the systems are unknown prior to clustering, we use an unsupervised clustering method that uses the Chinese restaurant process implemented using Gibbs sampling. The system identification is divided into two parts: training and online classification. In the training phase, we assign wireless systems present in the surrounding to the clusters while the online classification uses this trained data to perform classification. By means of an extensive measurement campaign, we show that the proposed machine learning process achieves up to 90% correctness in classifying the wireless systems considered here.