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The Coronavirus disease (COVID-19) pandemic has caused social and economic crisis to the globe. Contact tracing is a proven effective way of containing the spread of COVID-19. In this paper, we propose CAPER, a Cellular-Assisted deeP lEaRning based COVID-19 contact tracing system based on cellular network channel state information (CSI) measurements. CAPER leverages a deep neural network based feature extractor to map cellular CSI to a neural network feature space, within which the Euclidean distance between points strongly correlates with the proximity of devices. By doing so, we maintain user privacy by ensuring that CAPER never propagates one client's CSI data to its server or to other clients. We implement a CAPER prototype using a software defined radio platform, and evaluate its performance in a variety of real-world situations including indoor and outdoor scenarios, crowded and sparse environments, and with differing data traffic patterns and cellular configurations in common use. Microbenchmarks show that our neural network model runs in 12.1 microseconds on the OnePlus 8 smartphone. End-to-end results demonstrate that CAPER achieves an overall accuracy of 93.39%, outperforming the accuracy of BLE based approach by 14.96%, in determining whether two devices are within six feet or not, and only misses 1.21% of close contacts. CAPER is also robust to environment dynamics, maintaining an accuracy of 92.35% after running for ten days. 

The coronavirus disease (COVID-19) pandemic has caused social and economic upheaval around the world. Contact tracing is a proven effective way that health authorities may contain the spread of COVID-19, but is challenging for airborne disease. In this paper, we propose LTESafe, a cellular-assisted privacy-preserving COVID-19 contact tracing system. LTESafe leverages a deep neural network based feature extractor to map the cellular CSI to a high-dimensional feature space, within which the Euclidean distance between points indicates the proximity of devices. By doing so, we preserve user privacy by hiding the physical locations of smartphones and at the same time achieve high accuracy. Our preliminary experimental results demonstrate that LTESafe achieves an overall accuracy of 92.79% in determining whether two devices are within six feet proximity or not, and only misses 1.35% of close contacts.