DEEP LEARNING FOR LOCATION BASED BEAMFORMING WITH NLOS CHANNELS - Archive ouverte HAL Accéder directement au contenu
Pré-Publication, Document De Travail Année : 2021

DEEP LEARNING FOR LOCATION BASED BEAMFORMING WITH NLOS CHANNELS

Résumé

Massive MIMO systems are highly efficient but critically rely on accurate channel state information (CSI) at the base station in order to determine appropriate precoders. CSI acquisition requires sending pilot symbols which induce an important overhead. In this paper, a method whose objective is to determine an appropriate precoder from the knowledge of the user's location only is proposed. Such a way to determine precoders is known as location based beamforming. It allows to reduce or even eliminate the need for pilot symbols, depending on how the location is obtained. the proposed method learns a direct mapping from location to precoder in a supervised way. It involves a neural network with a specific structure based on random Fourier features allowing to learn functions containing high spatial frequencies. It is assessed empirically and yields promising results on realistic synthetic channels. As opposed to previously proposed methods, it allows to handle both line-of-sight (LOS) and non-line-of-sight (NLOS) channels.
Fichier principal
Vignette du fichier
poc_prec_hal.pdf (1007.57 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03503074 , version 1 (27-12-2021)

Identifiants

Citer

Luc Le Magoarou, Taha Yassine, Stéphane Paquelet, Matthieu Crussière. DEEP LEARNING FOR LOCATION BASED BEAMFORMING WITH NLOS CHANNELS. 2021. ⟨hal-03503074⟩
82 Consultations
212 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More