Global Scale Self-Supervised Channel Charting with Sensor Fusion - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2024

Global Scale Self-Supervised Channel Charting with Sensor Fusion

Résumé

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.

Dates et versions

hal-04617877 , version 1 (19-06-2024)

Identifiants

Citer

Omid Esrafilian, Mohsen Ahadi, Florian Kaltenberger, David Gesbert. Global Scale Self-Supervised Channel Charting with Sensor Fusion. GLOBECOM 2024, IEEE Global Communications Conference, IEEE, Dec 2024, Cape Town, South Africa. ⟨hal-04617877⟩

Collections

EURECOM
0 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Mastodon Facebook X LinkedIn More