Global Scale Self-Supervised Channel Charting with Sensor Fusion

TL;DR

This paper introduces a self-supervised channel charting method that fuses sensor data and TDOA measurements to achieve sub-meter localization accuracy, advancing radio-based sensing for 6G applications.

Contribution

It presents a novel sensor fusion approach with self-supervised learning for channel charting, improving localization accuracy without requiring ground truth or geometric models.

Findings

Achieves 90% sub-meter localization accuracy

Outperforms existing channel charting techniques

Validates effectiveness through simulations

Abstract

The sensing and positioning capabilities foreseen in 6G have great potential for technology advancements in various domains, such as future smart cities and industrial use cases. Channel charting has emerged as a promising technology in recent years for radio frequency-based sensing and localization. However, the accuracy of these techniques is yet far behind the numbers envisioned in 6G. To reduce this gap, in this paper, we propose a novel channel charting technique capitalizing on the time of arrival measurements from surrounding Transmission Reception Points (TRPs) along with their locations and leveraging sensor fusion in channel charting by incorporating laser scanner data during the training phase of our algorithm. The proposed algorithm remains self-supervised during training and test phases, requiring no geometrical models or user position ground truth. Simulation results validate the achievement of a sub-meter level localization accuracy using our algorithm 90% of the time, outperforming the state-of-the-art channel charting techniques and the traditional triangulation-based approaches.