102 citations · 194 across the 25 of their papers we have counts for
17 papers · 1 filter
Quantized Transformers for Massive MIMO Precoding with Automatic Resolution Tuning
Ghazal Kasalaee, Glodi Sala Mangituka, Ali Hasanzadeh Karkan +2
Deep learning precoders, particularly those based on Transformer architectures, offer superior spectral efficiency for Massive MIMO systems but may incur high computational cost. W…
Is Forecasting Accuracy Enough? A Comparative Study of Traffic Forecasters for Beam-Hopping LEO Satellite Networks
Yekta Demirci, Guillaume Mantelet, Stéphane Martel +2
We evaluate diverse models for user traffic demand forecasting in Low Earth Orbit (LEO) satellite networks with Beam Hopping (BH), questioning whether predictive accuracy is the ri…
The Impact of Demand Forecasting on Delay and Jitter in DVB-Based Beam-Hopping LEO Networks
Yekta Demirci, Guillaume Mantelet, Stéphane Martel +2
In LEO satellite networks utilizing beam hopping (BH), resource allocation plans must be committed well in advance. This inherent operational delay necessitates predicting future u…
Burst Aware Forecasting of User Traffic Demand in LEO Satellite Networks
Yekta Demirci, Guillaume Mantelet, Stephane Martel +2
In Low Earth Orbit (LEO) satellite networks, Beam Hopping (BH) technology enables the efficient utilization of limited radio resources by adapting to varying user demands and link…
Forecasting Self-Similar User Traffic Demand Using Transformers in LEO Satellite Networks
Yekta Demirci, Guillaume Mantelet, Stéphane Martel +2
In this paper, we propose the use of a transformer-based model to address the need for forecasting user traffic demand in the next generation Low Earth Orbit (LEO) satellite networ…
A Foundation Model for Massive MIMO Precoding with an Adaptive per-User Rate-Power Tradeoff
Jérôme Emery, Ali Hasanzadeh Karkan, Jean-François Frigon +1
Deep learning (DL) has emerged as a solution for precoding in massive multiple-input multiple-output (mMIMO) systems due to its capacity to learn the characteristics of the propaga…
Compression of Site-Specific Deep Neural Networks for Massive MIMO Precoding
Ghazal Kasalaee, Ali Hasanzadeh Karkan, Jean-François Frigon +1
The deployment of deep learning (DL) models for precoding in massive multiple-input multiple-output (mMIMO) systems is often constrained by high computational demands and energy co…
A Low-Complexity Plug-and-Play Deep Learning Model for Massive MIMO Precoding Across Sites
Ali Hasanzadeh Karkan, Ahmed Ibrahim, Jean-François Frigon +1
Massive multiple-input multiple-output (mMIMO) technology has transformed wireless communication by enhancing spectral efficiency and network capacity. This paper proposes a novel…
Adaptive Detection of On-Orbit Jamming for Securing GEO Satellite Links
Anouar Boumeftah, Olfa Ben Yahia, Jean-François Frigon +2
This paper introduces a scenario where a maneuverable satellite in geostationary orbit (GEO) conducts on-orbit attacks, targeting communication between a GEO satellite and a ground…
A Scalable Architecture for Future Regenerative Satellite Payloads
Olfa Ben Yahia, Zineb Garroussi, Brunilde Sansò +4
This paper addresses the limitations of current satellite payload architectures, which are predominantly hardware-driven and lack the flexibility to adapt to increasing data demand…
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…
Evolution of High Throughput Satellite Systems: Vision, Requirements, and Key Technologies
Olfa Ben Yahia, Zineb Garroussi, Olivier Bélanger +5
High throughput satellites (HTS), with their digital payload technology, are expected to play a key role as enablers of the upcoming 6G networks. HTS are mainly designed to provide…
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,…
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…
System Energy-Efficient Hybrid Beamforming for mmWave Multi-user Systems
Vu Nguyen Ha, Duy H. N. Nguyen, Jean-Francois Frigon
This paper develops energy-efficient hybrid beamforming designs for mmWave multi-user systems where analog precoding is realized by switches and phase shifters such that radio freq…