123 citations · 621 across the 25 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…
Optimal Privacy Preserving for Federated Learning in Mobile Edge Computing
Hai M. Nguyen, Nam H. Chu, Diep N. Nguyen +5
Federated Learning (FL) with quantization and deliberately added noise over wireless networks is a promising approach to preserve user differential privacy (DP) while reducing wire…
Automatic Machine Learning for Multi-Receiver CNN Technology Classifiers
Amir-Hossein Yazdani-Abyaneh, Marwan Krunz
Convolutional Neural Networks (CNNs) are one of the most studied family of deep learning models for signal classification, including modulation, technology, detection, and identifi…