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Publikation

TraPos: A Transformer-Based Localization Framework for Next-Generation Mobile Networks

Till Ruppert; Florian Langenstein; Dennis Salzmann; Florian Herrmann; Christoph Fischer; Michael Gundall; Hans Dieter Schotten (Hrsg.)
IEEE International Conference on Communications (ICC-2026), IEEE, 2026.

Zusammenfassung

In multipath-rich indoor environments, accurate localization remains a challenging task, as bandwidth-limited signals require high-resolution algorithms for precise parameter estimation. To address this challenge, we introduce TraPos, a transformer-based framework for indoor localization. The network is trained and evaluated on 5G channel measurements collected with a software-defined radio (SDR) platform. The datasets were acquired using an automated guided vehicle (AGV), with spatial uniformity ensured through grid-based filtering. As a baseline, TraPos is compared to a state-of-the- art joint angle-delay Multiple Signal Classification (MUSIC) algorithm. Results show that TraPos outperforms the super- resolution method in both angle of arrival (AOA) and time difference of arrival (TDOA) estimation. When trained directly on two-dimensional coordinates, TraPos achieves decimeter-level accuracy, demonstrating competitive performance for unfiltered position estimates.

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