8 papers
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…
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…
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…