IEEE DLT: Self-Supervised Learning for Wi-Fi Sensing: Trends, Challenges, and Outlook
Challenges, and Outlook
Abstract: Wi-Fi signals, traditionally used for data transmissions
in wireless local area networks (WLANs), are now being explored for
sensing the environment. With Wi-Fi widely available in public and
private spaces, it presents advantages over other sensing methods,
such as preserving privacy and working in low-light conditions.
Wi-Fi sensing is cost-effective as it builds on the existing Wi-Fi
infrastructure with a variety of applications including proximity
detection, localization, human activity recognition, and health
monitoring. The talk will first highlight the evolution of Wi-Fi
standards alongside the most recent IEEE 802.11bf which is under
study to develop protocols across all spectrum bands,
including sub-7 GHz (2.4 GHz, 5 GHz, 6 GHz) and the 60 GHz mmWave
band. Fundamental concepts related to Wi-Fi sensing, such as channel
state information (CSI), CSI measurement and data collection
procedures, and CSI pre-processing methods, will then be covered. A
comparative analysis of existing Wi-Fi sensing datasets will be
presented. Recent deep learning approaches in Wi-Fi sensing will be
discussed, with particular emphasis on the role of self-supervised
learning (SSL). The mechanics of contrastive and non-contrastive SSL
solutions will be examined, and a quantitative comparative analysis
in terms of classification accuracy will be provided. Lastly,
emerging technologies that can be leveraged to enhance Wi-Fi sensing
performance will be identified.
Date and Time
Location
Hosts
Registration
- Date: 23 May 2025
- Time: 04:30 PM UTC to 06:30 PM UTC
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- Queen's University
- 19 Division St, Kingston, ON
- Kingston, Ontario
- Canada K7L 2N9
- Building: Dupuis Hall
- Room Number: Dupuis 215
Speakers
Hina of York University
Talk Title 1: Self-Supervised Learning for Wi-Fi Sensing: Trends, Challenges, and Outlook
Abstract: Wi-Fi signals, traditionally used for data transmissions
in wireless local area networks (WLANs), are now being explored for
sensing the environment. With Wi-Fi widely available in public and
private spaces, it presents advantages over other sensing methods,
such as preserving privacy and working in low-light conditions.
Wi-Fi sensing is cost-effective as it builds on the existing Wi-Fi
infrastructure with a variety of applications including proximity
detection, localization, human activity recognition, and health
monitoring. The talk will first highlight the evolution of Wi-Fi
standards alongside the most recent IEEE 802.11bf which is under
study to develop protocols across all spectrum bands,
including sub-7 GHz (2.4 GHz, 5 GHz, 6 GHz) and the 60 GHz mmWave
band. Fundamental concepts related to Wi-Fi sensing, such as channel
state information (CSI), CSI measurement and data collection
procedures, and CSI pre-processing methods, will then be covered. A
comparative analysis of existing Wi-Fi sensing datasets will be
presented. Recent deep learning approaches in Wi-Fi sensing will be
discussed, with particular emphasis on the role of self-supervised
learning (SSL). The mechanics of contrastive and non-contrastive SSL
solutions will be examined, and a quantitative comparative analysis
in terms of classification accuracy will be provided. Lastly,
emerging technologies that can be leveraged to enhance Wi-Fi sensing
performance will be identified.
Biography:
Hina Tabassum, received the Ph.D. degree from the King Abdullah University of Science and Technology (KAUST). She is currently an Associate Professor with the Lassonde School of Engineering, York University, Canada, where she joined as an Assistant Professor, in 2018. She is also appointed as a Visiting Faculty at University of Toronto in 2024 and the York Research Chair of 5G/6G-enabled mobility and sensing applications in 2023, for five years. Prior to that, she was a postdoctoral research associate at University of Manitoba, Canada. She has been selected as IEEE ComSoc Distinguished Lecturer (2025-2026). She is listed in the Stanford’s list of the World’s Top Two-Percent Researchers in 2021-2024. She received the Lassonde Innovation Early-Career Researcher Award in 2023 and the N2Women: Rising Stars in Computer Networking and Communications in 2022. She has been recognized as an Exemplary Editor by the IEEE Communications Letters (2020), IEEE Open Journal of the Communications Society (IEEE OJCOMS) (2023-2024), and IEEE Transactions on Green Communications and Networking (2023). She was recognized as an Exemplary Reviewer (Top 2% of all reviewers) by IEEE Transactions on Communications in 2015, 2016, 2017, 2019, and 2020. She is the Founding Chair of the Special Interest Group on THz communications in IEEE Communications Society (ComSoc)-Radio Communications Committee (RCC). She served as an Associate Editor for IEEE Communications Letters (2019-2023), IEEE OJCOMS (2019-2023), and IEEE Transactions on Green Communications and Networking (2020-2023). Currently, she is also serving as an Area Editor for IEEE OJCOMS and an Associate Editor for IEEE Transactions on Communications, IEEE Transactions on Wireless Communications, IEEE Transactions on Mobile Computing, and IEEE Communications Surveys & Tutorials.
Address:York University,
12:30 PM: Pizza lunch
1 PM: Distinguished Lecture (DL)