Channel charting (CC) is an emerging machine learning framework that aims at learning
lower-dimensional representations of the radio geometry from collected channel state …

Abstract Background and Objectives Graph neural networks (GNN) have demonstrated
remarkable encoding capabilities in the context of brain network classification tasks. They …

Spatial consistency, meaning the spatial correlation of small scale fading, is a recently
added feature in the geometry-based stochastic channel model used by 3rd Generation …

We consider a machine learning approach for beam handover in mmWave 5G New Radio
systems, in which User Equipments (UEs) perform autonomous beam selection, conditioned …

Massive multiple-input and multiple-output is widely considered as a key enabler of the next
generation 5G networks. With a large number of antennas at the base station, both spectral …

Exploiting massive multiple-input-multiple-output (MIMO) gains come at the expense of
obtaining accurate channel estimates at the base station. However, conventional channel …

This work proposes a framework for multiuser massive Multiple Input Multiple Output (MIMO)
systems which is composed of three parts-clustering, grouping, and scheduling-and aims at …

Sub-THz communication is emerging as a promising technology to improve the cellular
system capacity in the sixth-generation (6G) era. Compared to conventional microwave …

5G est prévu pour s' attaquer, en plus d'une augmentation considérable du volume de trafic,
la tâche de connecter des milliards d'appareils avec des exigences de service hétérogènes …

Thanks to its slow-varying characteristic and relatively low requirement for estimation
overhead, the covariance matrix has been extensively researched in sixth-generation (6G) …