Angle-Delay Features and Distances for Channel Charting

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A4 Artikkeli konferenssijulkaisussa

Date

2024

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Mcode

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Language

en

Pages

6

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2024 IEEE Wireless Communications and Networking Conference, WCNC 2024 - Proceedings, IEEE Wireless Communications and Networking Conference, WCNC

Abstract

Channel charting (CC) is an unsupervised machine learning framework for learning a lower-dimensional representation of Channel State Information (CSI), while preserving spatial relations between CSI samples. In this paper, we consider super-resolution features in the angle-delay domain in massive Multiple-Input Multiple-Output (MIMO) systems. We i) treat the angle and delay separately, ii) present the so-called 'Normalized Polar Feature' utilizing the channel statistics of the CSI samples, iii) use the Euclidean distance to compute the dissimilarity matrix, and create the channel chart. Simulation results based on the DeepMIMO data-set show that the proposed super-resolution representation with the Euclidean distance leads to the state-of-the-art quality CC as compared to other CSI features and distances from the literature such as angle-delay-power features with earth mover distance.

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Publisher Copyright: © 2024 IEEE.

Keywords

Channel charting, channel state information, feature distance, multi-path components, super-resolution feature

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Citation

Uykan, Z, Al-Tous, H, Yiğitler, H, Jäntti, R & Tirkkonen, O 2024, Angle-Delay Features and Distances for Channel Charting . in 2024 IEEE Wireless Communications and Networking Conference, WCNC 2024 - Proceedings . IEEE Wireless Communications and Networking Conference, WCNC, IEEE, IEEE Wireless Communications and Networking Conference, Dubai, United Arab Emirates, 21/04/2024 . https://doi.org/10.1109/WCNC57260.2024.10571176