94 DiverSense: Maximizing Wi-Fi Sensing Range Leveraging Signal Diversity YANG LI, Peking University, China DAN WU, Peking University, China JIE ZHANG, University of Science and Technology Beijing, China and University of Leeds, United Kingdom XUHAI XU, University of Washington, United States YAXIONG XIE, Princeton University, United States TAO GU, Macquarie University, Australia DAQING ZHANG ∗ , Peking University, China and Institut Polytechnique de Paris, France 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 refections from the human target and thus only supports a limited sensing range, which signifcantly 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 diferent 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 foor, 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. CCS Concepts: · Human-centered computing → Ubiquitous and mobile computing systems and tools. Additional Key Words and Phrases: Wireless Sensing, Sensing Range, Channel State Information (CSI) ACM Reference Format: Yang Li, Dan Wu, Jie Zhang, Xuhai Xu, Yaxiong Xie, Tao Gu, and Daqing Zhang. 2022. DiverSense: Maximizing Wi-Fi Sensing Range Leveraging Signal Diversity. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 6, 2, Article 94 ( June 2022), 28 pages. https://doi.org/10.1145/3536393 ∗ This is the corresponding author. Authors’ addresses: Yang Li, School of Computer Science, Peking University, Beijing, China, liyang@stu.pku.edu.cn; Dan Wu, School of Computer Science, Peking University, Beijing, China; Jie Zhang, University of Science and Technology Beijing, Beijing, China and University of Leeds, Leeds, United Kingdom; Xuhai Xu, University of Washington, Seattle, United States; Yaxiong Xie, Princeton University, Princeton, United States; Tao Gu, Macquarie University, Sydney, Australia; Daqing Zhang, School of Computer Science, Peking University, Beijing, China, Telecom SudParis and Institut Polytechnique de Paris, Paris, France, dqzhang@sei.pku.edu.cn. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for proft or commercial advantage and that copies bear this notice and the full citation on the frst page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specifc permission and/or a fee. Request permissions from permissions@acm.org. © 2022 Association for Computing Machinery. 2474-9567/2022/6-ART94 $15.00 https://doi.org/10.1145/3536393 Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., Vol. 6, No. 2, Article 94. Publication date: June 2022.