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Adversarial Robustness of Distilled and Pruned Deep Learning-based Wireless Classifiers
Data-driven deep learning (DL) techniques developed for automatic modulation classification (AMC) of wireless signals are vulnerable to adversarial attacks.
Edge-Efficient Deep Learning Models for Automatic Modulation Classification: A Performance Analysis
The recent advancement in deep learning (DL) for automatic modulation classification (AMC) of wireless signals has encouraged numerous possible applications on resource-constrained edge devices.
Downlink Power Allocation in Massive MIMO via Deep Learning: Adversarial Attacks and Training
The successful emergence of deep learning (DL) in wireless system applications has raised concerns about new security-related challenges.
Universal Adversarial Attacks on Neural Networks for Power Allocation in a Massive MIMO System
Deep learning (DL) architectures have been successfully used in many applications including wireless systems.
Moving Object Classification with a Sub-6 GHz Massive MIMO Array using Real Data
Classification between different activities in an indoor environment using wireless signals is an emerging technology for various applications, including intrusion detection, patient care, and smart home.
Adversarial Attacks on Deep Learning Based Power Allocation in a Massive MIMO Network
In this paper, we extend this to regression problems and show that adversarial attacks can break DL-based power allocation in the downlink of a massive multiple-input-multiple-output (maMIMO) network.
