DiverSense: Maximizing Wi-Fi Sensing Range Leveraging Signal Diversity

Abstract

The ubiquity of Wi-Fi infrastructure has facilitated the development of a range of Wi-Fi based sensing applications. Wi-Fi sensing relies on weak signal reflections from the human target and thus only supports a limited sensing range, which significantly hinders the real-world deployment of the proposed sensing systems. To extend the sensing range, traditional algorithms focus on suppressing the noise introduced by the imperfect Wi-Fi hardware. This paper picks a different direction and proposes to enhance the quality of the sensing signal by fully exploiting the signal diversity provided by the Wi-Fi hardware. We propose DiverSense, a system that combines sensing signal received from all subcarriers and all antennas in the array, to fully utilize the spatial and frequency diversity. To guarantee the diversity gain after signal combining, we also propose a time-diversity based signal alignment algorithm to align the phase of the multiple received sensing signals. We implement the proposed methods in a respiration monitoring system using commodity Wi-Fi devices and evaluate the performance in diverse environments. Extensive experimental results demonstrate that DiverSense is able to accurately monitor the human respiration even when the sensing signal is under noise floor, and therefore boosts sensing range to 40 meters , which is a 3 × improvement over the current state-of-the-art. DiverSense also works robustly under NLoS scenarios, e.g. , DiverSense is able to accurately monitor respiration even when the human and the Wi-Fi transceivers are separated by two concrete walls with wooden doors. between transceivers and the distance between transceivers is 11m. We close the door during the experiment. We ask the subject to sit in Room A (S1, S3, S4, S5) and breath normally. Results show that the mean absolute error is 0.15bpm, 0.09bpm, 0.16bpm, 0.22bpm, respectively. We then move Tx to T5 and there are