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Channel Charting for Position and Orientation
arXiv Math
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이 매체는 공공·자유 라이선스로 본문을 직접 표시합니다.Electrical Engineering and Systems Science > Signal Processing
[Submitted on 16 Jun 2026]
Title:Channel Charting for Position and Orientation
View PDF HTML (experimental)Abstract:Channel charting (CC) in real-world coordinates is a recently proposed self-supervised machine learning method that maps high-dimensional channel state information (CSI) to user equipment (UE) position. In this paper, we extend CC to also estimate UE orientation, which can further assist tasks such as beamfinding, precoding, and beam- and cell-assignment. To this end, we propose a novel orientation triplet loss that accounts for angle periodicity and an alignment loss that embeds estimated orientations in real-world coordinates in a self-supervised fashion. Using real-world CSI measurements from a standard-compliant 5G NR system, we demonstrate that the proposed method achieves position and orientation estimation accuracy close to that of supervised approaches trained with ground-truth labels.
Submission history
From: Reinhard Wiesmayr [view email][v1] Tue, 16 Jun 2026 16:48:31 UTC (4,724 KB)
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