Over-the-air computation via reconfigurable intelligent surface

W Fang, Y Jiang, Y Shi, Y Zhou… - IEEE transactions on …, 2021 - ieeexplore.ieee.org
Over-the-air computation (AirComp) is a disruptive technique for fast wireless data
aggregation in Internet of Things (IoT) networks via exploiting the waveform superposition …

Machine learning-enabled joint antenna selection and precoding design: From offline complexity to online performance

TX Vu, S Chatzinotas, VD Nguyen… - IEEE Transactions …, 2021 - ieeexplore.ieee.org
We investigate the performance of multi-user multiple-antenna downlink systems in which a
base station (BS) serves multiple users via a shared wireless medium. In order to fully …

Sparse array beamforming design for wideband signal models

SA Hamza, MG Amin - IEEE Transactions on Aerospace and …, 2020 - ieeexplore.ieee.org
We develop sparse array receive beamformer design methods achieving maximum signal-to-
interference plus noise ratio for wideband sources and jammers. Both tapped delay line …

Reconfigurable intelligent surface-assisted secret key generation in spatially correlated channels

L Hu, G Li, X Qian, A Hu… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Reconfigurable intelligent surface (RIS) is a disruptive technology to enhance the
performance of physical-layer key generation (PKG) thanks to its ability to smartly customize …

Learning-based antenna selection for multicasting

MS Ibrahim, AS Zamzam, X Fu… - 2018 IEEE 19th …, 2018 - ieeexplore.ieee.org
In multi-antenna systems, it is preferred to activate only a subset of the available transmit
antennas in order to save hardware and energy resources, without seriously degrading the …

Fast algorithms for joint multicast beamforming and antenna selection in massive MIMO

MS Ibrahim, A Konar… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Massive MIMO is currently a leading physical layer technology candidate that can
dramatically enhance throughput in 5G systems, for both unicast and multicast transmission …

Sparse array design for maximizing the signal-to-interference-plus-noise-ratio by matrix completion

SA Hamza, MG Amin - Digital Signal Processing, 2020 - Elsevier
We consider sparse array beamformer design achieving maximum signal-to interference
plus noise ratio (MaxSINR). Both array configuration and weights are attuned to the …

Sparse Array Design for Optimum Beamforming Using Deep Learning

SA Hamza, K Juretus, MG Amin - Sparse Arrays for Radar …, 2024 - Wiley Online Library
The chapter considers sparse array beamforming design via machine learning with the
learning objective of maximum signal‐to‐interference plus noise ratio (MaxSINR). The …

Deep Learning of the Sparse Array Configurations in Optimum Beamforming

K Juretus, MG Amin, SA Hamza - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
The paper examines neural network learning of the sparse array configurations in optimum
beamforming. Unlike iterative greedy, convex, and global optimization methods for optimum …

Sparse array design utilizing matrix completion

SA Hamza, MG Amin - 2019 53rd Asilomar Conference on …, 2019 - ieeexplore.ieee.org
Sparse array design has been advantageous in re¬ ducing receiver data, system's hardware
and computational costs by the careful placement of available sensors such that the ob¬ …