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Communication Dans Un Congrès Année : 2022

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 howthe 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.

Dates et versions

hal-03968183 , version 1 (01-02-2023)

Identifiants

Citer

Luc Le Magoarou, Taha Yassine, Stéphane Paquelet, Matthieu Crussière. Deep Learning for Location Based Beamforming with Nlos Channels. 47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2022, Singapore, Singapore. ⟨10.1109/ICASSP43922.2022.9747293⟩. ⟨hal-03968183⟩
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