A Framework for Wireless Technology Classification using Crowdsensing Platforms

Abstract

Spectrum crowdsensing systems do not provide labeled data near real-time yet. We propose a framework that addresses this challenge and relies solely on Power Spectrum Density (PSD) data collected by low-cost receivers. A major hurdle is to design a system that is computationally efficient for near real-time operation, yet using only the limited 2 MHz bandwidth of low-cost spectrum sensors. First, we present a method for unsupervised transmission detection that works with PSD data already collected by the backend of the crowdsensing platform, and that provides stable detection of transmission boundaries. Second, we introduce a data-driven deep learning solution to classify the wireless technology used by the transmitter, using transmission features in a compressed space extracted from single PSD measurements over at most 2 MHz band. We build an experimental platform, and evaluate our framework with real-world data collected from 47 different sensors deployed across Europe. We show that our framework yields an average classification accuracy close to 94.25% over the testing dataset, with a maximum latency of 3.4 seconds when integrated in the backend of a major crowdsensing network. Code and data have been released for reproducibility and further studies.