102 citations · 194 across the 25 of their papers we have counts for
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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…
A Low-Complexity Plug-and-Play Deep Learning Model for Generalizable Massive MIMO Precoding
Ali Hasanzadeh Karkan, Ahmed Ibrahim, Jean-François Frigon +1
Massive multiple-input multiple-output (mMIMO) downlink precoding offers high spectral efficiency but remains challenging to deploy in practice because near-optimal algorithms such…
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…