ABSTRACT
Contact tracing is a key mechanism to help contain the COVID-19 pandemic and other pandemics in the future. In this work, we propose using 5G channel signatures - specifically, mm-Wave channel signatures - to perform contact tracing and infer early sources of the infection. Our network-side approach is motivated by the density of mm-Wave base stations, coupled with the large amount of data about mobile device signals already being collected by cellular operators. We model the contact tracing problem as a graph mining problem, and develop machine learning models to estimate contacts between UEs based on 5G channel signatures such as received power. These contacts are also used to infer the original sources of the infection. Simulations of our proposed method using the ns-3 5G mmWave module suggest that contact can be inferred with a recall of 85% and specificity of 94%. Our infection sources estimation method can accurately rank the most likely infection sources, with the true infection sources lying in the top 25% of the ranked list on average. These methods represent a first step towards network-based contact tracing, and can complement other contact tracing methods to help reduce the spread of disease. © 2022 IEEE.