48 citations · 95 across the 7 of their papers we have counts for
9 papers
Adversarial Attacks on Deep Learning Based Power Allocation in a Massive MIMO Network
B. R. Manoj, Meysam Sadeghi, Erik G. Larsson
Deep learning (DL) is becoming popular as a new tool for many applications in wireless communication systems. However, for many classification tasks (e.g., modulation classificatio…
Joint Unicast and Multi-group Multicast Transmission in Massive MIMO Systems
Meysam Sadeghi, Emil Björnson, Erik G. Larsson +2
We study the joint unicast and multi-group multicast transmission in massive multiple-input-multiple-output (MIMO) systems. We consider a system model that accounts for channel est…
Physical Adversarial Attacks Against End-to-End Autoencoder Communication Systems
Meysam Sadeghi, Erik G. Larsson
We show that end-to-end learning of communication systems through deep neural network (DNN) autoencoders can be extremely vulnerable to physical adversarial attacks. Specifically,…
Adversarial Attacks on Deep-Learning Based Radio Signal Classification
Meysam Sadeghi, Erik G. Larsson
Deep learning (DL), despite its enormous success in many computer vision and language processing applications, is exceedingly vulnerable to adversarial attacks. We consider the use…
Multigroup Multicast Precoding in Massive MIMO
Meysam Sadeghi, Emil Björnson, Erik G. Larsson +2
Optimal physical layer multicasting (PLM) is an NP-hard problem that for simplicity has been studied under idealistic assumptions, e.g., availability of perfect channel state infor…
Max-Min Fair Transmit Precoding for Multi-group Multicasting in Massive MIMO
Meysam Sadeghi, Emil Björnson, Erik G. Larsson +2
This paper considers the downlink precoding for physical layer multicasting in massive multiple-input-multiple-output (MIMO) systems. We study the max-min fairness (MMF) problem, w…