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Cited by 698

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

See through walls with WiFi!

Open access
Adib, Fadel, Katabi, Dina·2013·Proceedings of the ACM SIGCOMM 2013 conference on SIGCOMM

Abstract

Wi-Fi signals are typically information carriers between a transmitter and a receiver. In this paper, we show that Wi-Fi can also extend our senses, enabling us to see moving objects through walls and behind closed doors. In particular, we can use such signals to identify the number of people in a closed room and their relative locations. We can also identify simple gestures made behind a wall, and combine a sequence of gestures to communicate messages to a wireless receiver without carrying any transmitting device. The paper introduces two main innovations. First, it shows how one can use MIMO interference nulling to eliminate reflections off static objects and focus the receiver on a moving target. Second, it shows how one can track a human by treating the motion of a human body as an antenna array and tracking the resulting RF beam. We demonstrate the validity of our design by building it into USRP software radios and testing it in office buildings.

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Crossref · 10.1145/2486001.2486039

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Metadata

Authors
Adib, F., & Katabi, D.
Venue
Proceedings of the ACM SIGCOMM 2013 conference on SIGCOMM
Publisher
ACM
Published
2013-08-27
Pages
75-86
OpenAlex
W2129045955
Cited by
698
References
1
FWCI
67.15
Type
conference paper
Language
en
Open access
gold

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CoChat. (2026). Citation check: See through walls with WiFi! https://app.cochat.ai/papers/10.1145/2486001.2486039
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Adib, F., & Katabi, D. (2013). See through walls with WiFi! Proceedings of the ACM SIGCOMM 2013 conference on SIGCOMM. https://doi.org/10.1145/2486001.2486039

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.