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Conference Papers Year : 2022

Channel charting based beamforming

Abstract

Channel charting (CC) is an unsupervised learning method allowing to locate users relative to each other without reference. From a broader perspective, it can be viewed as a way to discover a low-dimensional latent space charting the channel manifold. In this paper, this latent modeling vision is leveraged together with a recently proposed location-based beamforming (LBB) method to show that channel charting can be used for mapping channels in space or frequency. Combining CC and LBB yields a neural network resembling an autoencoder. The proposed method is empirically assessed on a channel mapping task whose objective is to predict downlink channels from uplink channels.
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Dates and versions

hal-03882861 , version 1 (05-12-2022)
hal-03882861 , version 2 (20-12-2022)

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Luc Le Magoarou, Taha Yassine, Stephane Paquelet, Matthieu Crussière. Channel charting based beamforming. Asilomar Conference on Signals, Systems, and Computers, Oct 2022, Pacific Grove, United States. ⟨hal-03882861v1⟩
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