NGWN Research Lab

York University ranks 5th in MacleansEducation Rankings, Canada's Best Comprehensive Universities (2026)

Our research group at York works at the intersection of machine learning empowered resource allocation and mobility management solutions for the next generation multi-band wireless communications and sensing networks for applications like environment and climate monitoring, smart agriculture, etc.

Next Generation Wireless Networks (NGWN) Research Lab

Hina Tabassum

Associate Professor, PhD, P. Eng, SMIEEE

Graduate Program Director (Engineering)

Electrical Engineering and Computer Science

Lassonde School of Engineering

York university, Toronto, Canada

Office: LAS-2008

Phone: (416) 736-2100, ext. 20889

Email: hinat@yorku.ca

Official Webpage: YorkU Webpage

Google Scholar Citations

NGWN research lab is led by Dr. Hina Tabassum. She is currently an Associate Professor in the Department of Electrical Engineering and Computer Science (EECS), York University, Toronto, Canada, and Visiting Faculty at University of Toronto since 2025. She has been appointed as York research Chair (2023 - 2028) in 5G/6G-enabled mobility and sensing applications. She is listed in Stanford’s top 2% of the world’s top researchers in 2021, 2022, 2023, 2024, and 2025. She has been selected as IEEE ComSoc Distinguished Lecturer for the class of 2025-2026! She has been appointed as a member of the NSERC Discovery Grant Evaluation Group for Electrical and Computer Engineering for a three-year term (2025–2028). She has earned numerous distinctions, including the Top 10 N2Women Stars in Networking and Communications (2025), one of the 10 Rising Stars in “N2Women: Rising Stars in Networking and Communications List” in 2022, and received Lassonde Innovation Award in 2023.

She (co)-authored 120+ articles in top-tier IEEE journals, magazines, and conferences. She was a Research Associate in the area of "Modeling, Analysis, and Optimization of 5G Networks" at Department of Electrical and Computer Engineering, University of Manitoba, Canada, under the supervision of Prof. Ekram Hossain. She obtained her PhD in the area of "Interference Modeling and Management of Heterogeneous Wireless Communication Networks", in June 2013, under the supervision of Prof. Mohamed Slim Alouini. She received Bachelors of Electronics Engineering from NED University of Technology (NEDUET) and secure First Position among all engineering universities of Karachi, Pakistan for which she received Gold medals from NEDUET and from SIEMENS. She then joined R&D at Pakistan Space and Upper Atmosphere Research Commission (SUPARCO) and received Best Performance Award in 2009.

She is a registered member of

She is currently a PI NSERC Discovery Grant (2025-2030), NSERC RTI (2026-2027), GRE Stream-B (2026-2028), and Co-PI on the following two NSERC CREATE initiatives:

SMART (Smart Mobility Advanced Research & Training) [2025-2031] ($1.65 M)
GMD-MSTI (Geomagnetic Disturbance in Modern Societies and Technological Infrastructures) [2024-2030] ($1.65 M).

She was recognized as Exemplary Reviewer (Top 2% of all reviewers) by IEEE Transactions on Communications in 2015, 2016, 2017, 2019, and 2020. She was recognized as Exemplary Editor by IEEE Communications Letters in 2020, and Exemplary Editor by IEEE Transactions on Green Communications and Networking in 2023, and Best Editor by IEEE Open Journal on Communications Society in 2023.

She served as an Associate Editor in IEEE Open Journal of Communications Society (IEEE OJCOMS) (2019 - 2023), in IEEE Transactions on Green Communications (IEEE TGCN) (2020 - 2023), Associate Editor in IEEE Communications Surveys and Tutorials (IEEE COMST) (Sep 2021 -Aug 2025), IEEE Transactions on Mobile Computing (IEEE TMC) (Feb 2025 - Aug 2026), and in IEEE Communications Letters (IEEE COMML) (2019 - 2023) . She is currently serving as

Special Topics Development Editor, IEEE Wireless Communications Magazine (July 2026 -)
Area Editor in IEEE Communications Surveys and Tutorials (IEEE COMST) (Aug 2025 -)
Area Editor in IEEE Open Journal of Communications Society (IEEE OJCOMS) (June 2023 -)
Associate Editor in IEEE Transactions on Wireless Communications (IEEE TWC) (Nov. 2023 -)
Associate Editor in IEEE Transactions on Communications (IEEE TCOM) (June 2022 -)

Research Themes and Corresponding Research Articles

Wireless Sensing and Communications (Click Here for Publications)

Wireless sensing is a process of collecting and analyzing wireless channel state information (CSI) or received signal strength indicator (RSSI) to capture environment dynamics and enable various sensing services such as

human activity recognition
vital signs monitoring
indoor localization
motion sensing
Fall detection and crowd counting

Deep learning methods offer a solution to learn hidden CSI patterns and perform human activity recognition, indoor localization, and motion sensing.

https://github.com/bornabr/RSCNet

We introduce a novel multi-objective integrated sensing and communications (ISAC) framework to enable collaborative wireless sensing in conjunction with over-the-air federated-edge learning (OTA-FEEL). The framework enables multi-task OTA aggregation to handle sensing and learning simultaneously, while benefiting from dual-purpose uplink signals for both communications and target sensing. Numerical results demonstrate that the proposed dual-purpose OTA-FEEL-enabled collaborative ISAC framework enhances sensing accuracy without adversely affecting the performance of the primary OTA-FEEL task. While conventional single-shot collaborative sensing schemes are limited by the average error of local estimators, the proposed algorithm achieves the CRB of the considered problem.

Machine Learning for Communications, Sensing, and Medical Applications (Click Here for Publications)

EMForecaster is a novel deep learning architecture designed for forecasting electromagnetic field exposure. It leverages multi-scale temporal pattern processing through patching, reversible instance normalization, and dimension mixing. To quantify uncertainty, it integrates conformal prediction, ensuring a guaranteed coverage rate of 1−α. Additionally, it introduces a 'Trade-off Score' metric that balances forecast reliability with the width of prediction intervals. EMForecaster outperforms existing methods significantly—achieving 53.97% improvement over the Transformer model and 38.44% over the average baseline in point forecasting. In conformal forecasting, it delivers an optimal balance between interval width and coverage, surpassing the average baseline by 24.73% and the Transformer by 49.17% [https://github.com/xmootoo/emforecaster]

We introduce a novel unsupervised learning framework for solving constrained optimization problems with non-convex constraints. Central to our approach is a differentiable projection function that ensures zero constraint violation by mapping outputs directly onto the feasible solution space. By integrating this projection mechanism with a custom neural network and unsupervised training, our method effectively optimizes the primary objective. Applied to wireless network optimization, the framework demonstrates superior performance, faster convergence, and greater computational efficiency compared to genetic algorithms and existing projection-based techniques—all while consistently maintaining perfect constraint satisfaction. This generalizable approach offers a compelling solution for complex optimization problems where traditional methods often fall short in enforcing constraints and achieving high-quality results.

Mobility-Aware Connected and Autonomous Systems (Click Here for Publications)

We developed integrated 5G network – highway simulation testbed. We developed multi-objective reinforcement learning framework for joint network selection and speed optimization in a multi-band RF/THz network with known and unknown transportation and telecommunication preferences.

From traffic engineering perspective, it is critical to:

Optimize the speed of vehicles to maximize the traffic flow and minimize collisions. Higher speeds imply better traffic flow but increase collisions.

From communication engineering perspective, it is crucial to:

Optimize the speed of vehicles to maximize the data rates and minimize handoffs. Higher speeds imply more handoffs and reduced data rates.

Multi-band RF/THz/VLC Wireless Networks

Next Generation Multiple Access

Reconfigurable Intelligent Surfaces

Edge Computing, Backhauling, and Network Slicing

Energy Efficient Resource Allocation

Recent Updates and Activities: Tutorials, Panels, and Talks

May 2026: Delivered IEEE ComSoc Distinguished Lecture Tour across Vancouver, Victoria, Calgary, and Winnipeg
March 2026: Invited Distinguished Speaker "Unlocking the Power of Large Language Models in Wireless Networks" NeuGenNet20261st Inter
December 2025: Industry Panel Speaker, IPA-25 Quantum AI for Future Networks: Intelligence Beyond Classical Boundaries, at IEEE Global Communications (Globecom'25), December, 2025.

Aug-Sep 2025: Delivered IEEE ComSoc Distinguished Lecture Tour across Netherland, Germany, and Luxembourg

June 2025: Tutorial Speaker, Machine Learning for 6G Wireless Networks: Latest Advances, Fundamentals, and Applications, at IEEE Intl. Conference on Communications (ICC'25), June, 2025.

June 2025: Tutorial Speaker, Machine Learning for Resource Allocation in 6G Wireless Networks, at IEEE Vehicular Technology Conference (VTC-Spring'25), June, 2025.

May 2025: Tutorial Speaker, Machine Learning for 6G Network Resource Management and Semantic Communications, at IEEE International Conference on Machine Learning for Communication and Networking (ICMLCN), May 2025.