102 citations · 182 across the 6 of their papers we have counts for
6 papers
SAGE-HB: Swift Adaptation and Generalization in Massive MIMO Hybrid Beamforming
Ali Hasanzadeh Karkan, Hamed Hojatian, Jean-François Frigon +1
Deep learning (DL)-based solutions have emerged as promising candidates for beamforming in massive Multiple-Input Multiple-Output (mMIMO) systems. Nevertheless, it remains challeng…
Learning Energy-Efficient Hardware Configurations for Massive MIMO Beamforming
Hamed Hojatian, Zoubeir Mlika, Jérémy Nadal +2
Hybrid beamforming (HBF) and antenna selection are promising techniques for improving the energy efficiency~(EE) of massive multiple-input multiple-output~(mMIMO) systems. However,…
Flexible Unsupervised Learning for Massive MIMO Subarray Hybrid Beamforming
Hamed Hojatian, Jérémy Nadal, Jean-François Frigon +1
Hybrid beamforming is a promising technology to improve the energy efficiency of massive MIMO systems. In particular, subarray hybrid beamforming can further decrease power consump…
Decentralized Beamforming for Cell-Free Massive MIMO with Unsupervised Learning
Hamed Hojatian, Jeremy Nadal, Jean-Francois Frigon +1
Cell-free massive MIMO (CF-mMIMO) systems represent a promising approach to increase the spectral efficiency of wireless communication systems. However, near-optimal beamforming so…
Unsupervised Deep Learning for Massive MIMO Hybrid Beamforming
Hamed Hojatian, Jeremy Nadal, Jean-Francois Frigon +1
Hybrid beamforming is a promising technique to reduce the complexity and cost of massive multiple-input multiple-output (MIMO) systems while providing high data rate. However, the…
RSSI-Based Hybrid Beamforming Design with Deep Learning
Hamed Hojatian, Vu Nguyen Ha, Jérémy Nadal +2
Hybrid beamforming is a promising technology for 5G millimetre-wave communications. However, its implementation is challenging in practical multiple-input multiple-output (MIMO) sy…