Optimizing Multicarrier Multiantenna Systems for LoS Channel Charting

Taha Yassine (1, 2, 3) , Luc Le Magoarou (4, 5) , Matthieu Crussière (6) , Stephane Paquelet (2)

1 Artificial Intelligence
2 IRT b-com - Institut de Recherche Technologique b-com
3 Hypermedia - Domaine Hypermedia (IRT b<>com)
4 INSA Rennes - Institut National des Sciences Appliquées - Rennes
5 IETR - Institut d'Électronique et des Technologies du numéRique
6 IETR - Institut d'Électronique et des Technologies du numéRique

Channel charting (CC) consists in learning a mapping between the space of raw channel observations, made available from pilot-based channel estimation in multicarrier multiantenna system, and a low-dimensional space where close points correspond to channels of user equipments (UEs) close spatially. Among the different methods of learning this mapping, some rely on a distance measure between channel vectors. Such a distance should reliably reflect the local spatial neighborhoods of the UEs. The recently proposed phase-insensitive (PI) distance exhibits good properties in this regards, but suffers from ambiguities due to both its periodic and oscillatory aspects, making users far away from each other appear closer in some cases. In this paper, a thorough theoretical analysis of the said distance and its limitations is provided, giving insights on how they can be mitigated. Guidelines for designing systems capable of learning quality charts are consequently derived. Experimental validation is then conducted on synthetic and realistic data in different scenarios.

Mots clés: channel charting; dimensionality reduction; MIMO signal processing; machine learning

Domaines: Traitement du signal et de l'image [eess.SP]; Intelligence artificielle [cs.AI]; Apprentissage [cs.LG]; Machine Learning [stat.ML]

Taha Yassine, Luc Le Magoarou, Matthieu Crussière, Stephane Paquelet. Optimizing Multicarrier Multiantenna Systems for LoS Channel Charting. 2023. ⟨hal-04218039⟩