<div class="csl-bib-body">
<div class="csl-entry">Prüller, R., Langwieser, R., & Rupp, M. (2025). Indoor MIMO Channel Measurements of the Near-Field to Far-Field Transition in FR3. In <i>2025 19th European Conference on Antennas and Propagation (EuCAP)</i>. 2025 19th European Conference on Antennas and Propagation (EuCAP), Stockholm, Sweden. IEEE. https://doi.org/10.23919/EuCAP63536.2025.11000050</div>
</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/221618
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dc.description.abstract
This paper presents an in-depth study of the nearfield (NF) to far-field (FF) transition in MIMO systems from 6 GHz to 24 GHz, which largely coincides with the frequency range 3 (FR3). A high-resolution dataset is contributed, derived from a channel sounding campaign conducted using a virtual uniform linear array (ULA) in an indoor environment. To observe the NF to FF transition we varied the ULA size while maintaining a fixed distance between the transmitter and receiver. Our findings confirm that the Fraunhofer distance provides a reliable indicator for the onset of FF conditions, offering new insights into optimizing MIMO performance for future wireless communication networks. The dataset and analysis serve as a valuable resource for further exploration of NF propagation and can be used as a study item itself or to validate NF capable channel models.
en
dc.description.sponsorship
A1 Telekom Austria AG
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dc.language.iso
en
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dc.subject
Channel sounding
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dc.subject
Dataset
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dc.subject
Far-field
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dc.subject
Multiple-input multiple-output (MIMO)
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dc.subject
Near-field
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dc.subject
Virtual array
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dc.subject
Wireless channel
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dc.title
Indoor MIMO Channel Measurements of the Near-Field to Far-Field Transition in FR3
en
dc.type
Inproceedings
en
dc.type
Konferenzbeitrag
de
dc.relation.isbn
978-88-31299-10-7
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dc.relation.grantno
123
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dc.type.category
Full-Paper Contribution
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tuw.booktitle
2025 19th European Conference on Antennas and Propagation (EuCAP)
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tuw.peerreviewed
true
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tuw.relation.publisher
IEEE
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tuw.project.title
Modellgetriebene Optimierung unter Einsatz von maschinellem Lernen