Global Scale Self-Supervised Channel Charting with Sensor Fusion
This addresses the need for precise radio frequency-based localization in 6G applications like smart cities, representing a strong incremental improvement over prior channel charting techniques.
The paper tackles the problem of improving localization accuracy for 6G sensing by proposing a self-supervised channel charting technique that fuses time of arrival measurements with laser scanner data, achieving sub-meter accuracy 90% of the time and outperforming existing methods.
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.