Implicit Channel Charting with Application to UAV-aided Localization

TL;DR

This paper introduces a deep learning-based channel charting method that learns the physical geometry from CSI measurements, enabling accurate UAV-assisted localization without extensive data collection or line-of-sight constraints.

Contribution

It proposes a novel deep neural network approach that directly models physical geometry from channel state information, improving localization robustness and accuracy.

Findings

Outperforms existing channel charting methods in simulations

Enables accurate UAV localization in emergency scenarios

Reduces data collection costs compared to fingerprinting

Abstract

Traditional localization algorithms based on features such as time difference of arrival are impaired by non-line of sight propagation, which negatively affects the consistency that they expect among distance estimates. Instead, fingerprinting localization is robust to these propagation conditions but requires the costly collection of large data sets. To alleviate these limitations, the present paper capitalizes on the recently-proposed notion of channel charting to learn the geometry of the space that contains the channel state information (CSI) measurements collected by the nodes to be localized. The proposed algorithm utilizes a deep neural network that learns distances between pairs of nodes using their measured CSI. Unlike standard channel charting approaches, this algorithm directly works with the physical geometry and therefore only implicitly learns the geometry of the radio domain. Simulation results demonstrate that the proposed algorithm outperforms its competitors and allows accurate localization in emergency scenarios using an unmanned aerial vehicle.