Pinpointing Radio Devices: Robust Fingerprint Localization in Industrial Environments

Schösser A, Burmeister F, Bogner J, Li Z, Schulz P, Fettweis G, Franchi N (2026)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2026

Publisher: IEEE

Conference Proceedings Title: 2025 59th Asilomar Conference on Signals, Systems, and Computers

Event location: Pacific Grove, CA, USA US

URI: https://ieeexplore.ieee.org/abstract/document/11443652

DOI: 10.1109/IEEECONF67917.2025.11443652

Abstract

The massive availability of wireless links will shape the factory of the future. Many envisioned wireless use cases rely on ultra-reliable low latency communication (URLLC). While providing single devices with URLLC is trivial these days, scaling URLLC networks up to many users remains a challenge. Channel-aware radio resource allocation is a method to face this challenge efficiently. However, fine-granular channel awareness is required for channel-aware radio resource allocation. A recently proposed approach to address this requirement is to reuse the channel state information (CSI) stored in a radio environment map (REM). To successfully apply the information from the REM, very accurate localization of the radio device(s) is required, which is the focus of this paper. Based on measurement data and spatially extremely fine-grained fingerprint sequences, it is demonstrated that localizing an automated guided vehicle (AGV) as a radio device is possible with sub-centimeter accuracy on its trajectory. The performance is achieved with the k-nearest neighbor (kNN) approach, and its robustness, even in a time-varying environment, is highlighted. As an enabler for channel-aware resource allocation, the presented work represents a step towards scaling up URLLC networks in industrial use cases.

Authors with CRIS profile

Zhongju Li Lehrstuhl für Intelligente Technische Elektronik und Systeme Norman Franchi Lehrstuhl für Intelligente Technische Elektronik und Systeme

Involved external institutions

Technische Universität Dresden DE Germany (DE)

How to cite

APA:

Schösser, A., Burmeister, F., Bogner, J., Li, Z., Schulz, P., Fettweis, G., & Franchi, N. (2025). Pinpointing Radio Devices: Robust Fingerprint Localization in Industrial Environments. In 2025 59th Asilomar Conference on Signals, Systems, and Computers. Pacific Grove, CA, USA, US: IEEE.

MLA:

Schösser, Anton, et al. "Pinpointing Radio Devices: Robust Fingerprint Localization in Industrial Environments." Proceedings of the 59th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA IEEE, 2025.

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