activity
20132024
most citedDistributed Optimization for Energy-efficient Fog Computing in the Tactile Internet

123 citations · 621 across the 25 of their papers we have counts for

collaborators

25 papers

cs.DC2024

SANSee: A Physical-layer Semantic-aware Networking Framework for Distributed Wireless Sensing

Huixiang Zhu, Yong Xiao, Yingyu Li +2

Contactless device-free wireless sensing has recently attracted significant interest due to its potential to support a wide range of immersive human-machine interactive application…

cs.LG2024

Filtered Randomized Smoothing: A New Defense for Robust Modulation Classification

Wenhan Zhang, Meiyu Zhong, Ravi Tandon +1

Deep Neural Network (DNN) based classifiers have recently been used for the modulation classification of RF signals. These classifiers have shown impressive performance gains relat…

cs.IT2023★ 1 cited

Securing MIMO Wiretap Channel with Learning-Based Friendly Jamming under Imperfect CSI

Bui Minh Tuan, Diep N. Nguyen, Nguyen Linh Trung +5

Wireless communications are particularly vulnerable to eavesdropping attacks due to their broadcast nature. To effectively deal with eavesdroppers, existing security techniques usu…

cs.LG2023

RADIANCE: Radio-Frequency Adversarial Deep-learning Inference for Automated Network Coverage Estimation

Sopan Sarkar, Mohammad Hossein Manshaei, Marwan Krunz

Radio-frequency coverage maps (RF maps) are extensively utilized in wireless networks for capacity planning, placement of access points and base stations, localization, and coverag…

cs.LG2023★ 48 cited

Distributed Traffic Synthesis and Classification in Edge Networks: A Federated Self-supervised Learning Approach

Yong Xiao, Rong Xia, Yingyu Li +5

With the rising demand for wireless services and increased awareness of the need for data protection, existing network traffic analysis and management architectures are facing unpr…

cs.LG2023★ 37 cited

Time-sensitive Learning for Heterogeneous Federated Edge Intelligence

Yong Xiao, Xiaohan Zhang, Guangming Shi +3

Real-time machine learning has recently attracted significant interest due to its potential to support instantaneous learning, adaptation, and decision making in a wide range of ap…