Channel charting based beamforming

TL;DR

This paper introduces a novel method combining channel charting and location-based beamforming to map channels in space or frequency, using a neural network resembling an autoencoder for channel prediction.

Contribution

It presents a new approach that leverages channel charting with LBB to enable spatial and frequency channel mapping through a neural network autoencoder.

Findings

Effective channel mapping from uplink to downlink demonstrated

Neural network autoencoder successfully predicts channels in experiments

Method enhances spatial awareness in wireless communication systems

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.