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.
Cite
@article{arxiv.2212.12340,
title = {Channel charting based beamforming},
author = {Luc Le Magoarou and Taha Yassine and Stephane Paquelet and Matthieu Crussière},
journal= {arXiv preprint arXiv:2212.12340},
year = {2022}
}
Comments
Asilomar Conference on Signals, Systems, and Computers, Oct 2022, Pacific Grove, United States