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Found 5 papers, 1 papers with code
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.
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.
Physical Adversarial Attacks Against End-to-End Autoencoder Communication Systems
We show that end-to-end learning of communication systems through deep neural network (DNN) autoencoders can be extremely vulnerable to physical adversarial attacks.
Adversarial Attacks on Deep-Learning Based Radio Signal Classification
Deep learning (DL), despite its enormous success in many computer vision and language processing applications, is exceedingly vulnerable to adversarial attacks.
