48 citations · 96 across the 10 of their papers we have counts for
11 papers
Downlink Power Allocation in Massive MIMO via Deep Learning: Adversarial Attacks and Training
B. R. Manoj, Meysam Sadeghi, Erik G. Larsson
The successful emergence of deep learning (DL) in wireless system applications has raised concerns about new security-related challenges. One such security challenge is adversarial…
MRT-based Joint Unicast and Multigroup Multicast Transmission in Massive MIMO Systems
Meysam Sadeghi, Emil Björnson, Erik G. Larsson +2
We study joint unicast and multigroup multicast transmission in single-cell massive multiple-input-multiple-output (MIMO) systems, under maximum ratio transmission. For the unicast…
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…
Large System Analysis of Power Normalization Techniques in Massive MIMO
Meysam Sadeghi, Luca Sanguinetti, Romain Couillet +1
Linear precoding has been widely studied in the context of Massive multiple-input-multiple-output (MIMO) together with two common power normalization techniques, namely, matrix nor…
Hybrid Precoding for Multi-Group Physical Layer Multicasting
Meysam Sadeghi, Luca Sanguinetti, Chau Yuen
Next generation of wireless networks will likely rely on large-scale antenna systems, either in the form of massive multi-input-multi-output (MIMO) or millimeter wave (mmWave) syst…
Reducing the Computational Complexity of Multicasting in Large-Scale Antenna Systems
Meysam Sadeghi, Luca Sanguinetti, Romain Couillet +1
In this paper, we study the physical layer multicasting to multiple co-channel groups in large-scale antenna systems. The users within each group are interested in a common message…