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Collaborative Research: IMR: MM-1A: Functional Data Analysis-aided Learning Methods for Robust Wireless Measurements
List of Personnel
Principle Investigator
Graduate Students
Undergraduate Students
Project Description
With the increasing growth of large-scale, heterogeneous, dynamic, and complex wireless networks, how to achieve accurate and robust measurements in 5G networks and beyond becomes a challenging and important problem. Most existing data-driven solutions are black-box approaches, which may not be robust and adaptive, and work only for low-dimensional and discrete data. In fact, wireless data belong to the class of functional data, which can be represented by curves or functions. High-dimensional wireless datasets can be better handled by functional data analysis (FDA). Recognizing the significance of the aforementioned problems, this project aims to bridge the gap between FDA-based learning and wireless measurement.
The proposed research falls into the following four interwoven thrusts. (i) Functional Data Regression for Sparse Wireless Measurements: to develop a deep learning based approach to address fundamental regression problems of functional data. (ii) FDA-based Transfer Learning for DynamicWireless Measurements: to study transfer learning for functional data regression and classification under the distribution shift between test data and training data for effective wireless measurements in dynamic environments. (iii) Quantile FDA-based Learning for Robust Wireless Measurements and Control: to develop a deep learning-based approach to address the fundamental bottleneck of quantile regression-based methods. (iv) Wireless Measurement Applications for Integration and Validation.
Related Journal and Book Chapter Publications
Tianya Zhao, Xuyu Wang, Shiwen Mao, Slobodan Vucetic, and Jie Wu, “Adversarial deep learning for indoor localization,” Chapter X in Network Security Empowered by Artificial Intelligence.
Jing Hou, Xuyu Wang, and Amy Z Zeng, “Inter-Temporal Reward Strategies in the Presence of Strategic Ethical Hackers,” in IEEE/ACM Transactions on Networking, 2024.
Xiangyu Wang*, Xuyu Wang*, Shiwen Mao, Jian Zhang, Senthilkumar CG Periaswamy, and Justin Patton, “Adversarial deep learning for indoor localization,” IEEE Internet of Things Journal, vol.9, no.19, pp.18182--18194, Oct. 2022. DOI: 10.1109/JIOT.2022.3155562.. (*means co-first authors)
Xiangyu Wang, Xuyu Wang, Shiwen Mao, Jian Zhang, Senthilkumar CG Periaswamy, and Justin Patton, “Indoor radio map construction and localization with deep Gaussian Processes,” IEEE Internet of Things Journal, vol.7, no.11, pp. 11238-11249, Nov. 2020.
Related Conference Publications
Tianya Zhao, Ningning Wang, Guanqun Cao, Shiwen Mao, and Xuyu Wang, “Functional Data Analysis Assisted Cross-Domain Wi-Fi Sensing Using Few-Shot Learning,” in Proc. IEEE ICC 2024, Denver, CO, June 2024.
Nawrin Tabassum, Ka-Ho Chow, Xuyu Wang, Wenbin Zhang, and Yanzhao Wu, "On the Efficiency of Privacy Attacks in Federated Learning," in 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2024) Workshops - FedVision.
Tianya Zhao, Ningning Wang, Yanzhao Wu, Wenbin Zhang, and Xuyu Wang, “Backdoor Attacks Against Low-Earth Orbit Satellite Fingerprinting,” in Proc. IEEE INFOCOM 2024 workshop, Vancouver, Canada, May 2024.
Tianya Zhao, Xuyu Wang, Junqing Zhang, and Shiwen Mao, “Explanation-Guided Backdoor Attacks on Model-Agnostic RF Fingerprinting,” in Proc. IEEE INFOCOM 2024, Vancouver, Canada, May 2024.
Tianya Zhao, Xuyu Wang, and Shiwen Mao, "Cross-domain, Scalable, and Interpretable RF Device Fingerprinting,” in Proc. IEEE INFOCOM 2024, Vancouver, Canada, May 2024.
Tianya Zhao, Xuyu Wang, and Shiwen Mao, “Backdoor attacks against deep learning-based massive MIMO localization,” in Proc. IEEE GLOBECOM 2023, Kuala Lumpur, Malaysia, Dec. 2023.
Jing Hou, Xuyu Wang, and Amy Z Zeng, “Inter-Temporal Reward Decisions with Strategic Ethical Hackers,” in Proc. IEEE CNS 2023, Orlando, FL, Oct. 2023.
Hongpeng Jin, Wenqi Wei, Xuyu Wang, Wenbin Zhang, Yanzhao Wu, “Rethinking Learning Rate Tuning in the Era of Large Language Models,” will appear in IEEE CogMI 2023 Vision Track.
Open Wireless Localization, Sensing and Spectrum Dataset
Outreach Activities
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2024.06 I am invited to serve as a TPC co-chair of IEEE GLOBECOM 2024 Workshop on Machine Learning and Deep Learning for Wireless Security, Cape Town, South Africa in Dec. 2024. Please consider submitting your papers.
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2024.06 I am invited to serve as a TPC co-chair of IEEE GLOBECOM 2024 Workshop on Machine Learning and Deep Learning for Wireless Security, Cape Town, South Africa in Dec. 2024. Please consider submitting your papers.
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2024.06 I served as a co-chair for the IEEE ICC 2024 Workshop on Machine Learning and Deep Learning for Wireless Security in Denver, Colorado, USA on June 9, 2024.
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2023.10 I am invited to serve as a co-chair of IEEE INFOCOM 2024 Workshop on Deep Learning for Wireless Communications, Sensing, and Security (DeepWireless), Vancouver, Canada in May 2024. Please consider submitting your papers.
Award Information
This project is supported by the National Science Foundation (NSF) under Grant CNS-2319343 from 10/01/2023 to 09/30/2026. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the foundation. [NSF link with CNS-2319343]
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