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Dokumenttyp:
Forschungsdaten
Veröffentlichungsdatum:
01.03.2023
Verantwortlich:
Ayvasik, Serkut
Autorinnen / Autoren:
Ayvasik, Serkut; Mehmeti, Fidan; Babaians, Edwin; Kellerer, Wolfgang
Institutionszugehörigkeit:
TUM
Herausgeber:
TUM
Titel:
PEACH: Proactive and Environment Aware Channel State Information Prediction with Depth Images - Dataset
Identifikator:
doi:10.14459/2022mp1694552
Enddatum der Datenerzeugung:
16.05.2022
Fachgebiet:
ELT Elektrotechnik
Quellen der Daten:
Experimente und Beobachtungen / experiments and observations; Abbildungen von Objekten / image of objects
Andere Quellen der Daten:
Measurements of wireless raw signals in real-world indoor lab environment along with environment videos.
Datentyp:
Bilder / images; Programme und Anwendungen / software and applications
Anderer Datentyp:
signal measurements as binary data
Methode der Datenerhebung:
Measurement of 5G wireless I/Q samples over software defined radios with related environment video
Beschreibung:
PEACH dataset: 5G NR raw wireless measurements along with correlated videos of an indoor lab environment. The dataset contains 30 measurement runs obtained over 5 different days and each are taking about 3-4 minutes. The raw measurements are named after the receiver UHD models "X410" and "X310" resulting in total 60 raw measurement data. The dataset contains videos of each measurement for two cameras at different angles. Synchronized timestamps of cameras and transmitters are provided for furth...     »
Links:
This dataset relates to the publication: https://dl.acm.org/doi/10.1145/3579450
Schlagworte:
5G; wireless IQ data; software defined radio; indoor wireless measurement
Technische Hinweise:
View and download (3 TB total, 253 Files)
The data server also offers downloads with FTP
The data server also offers downloads with rsync (password m1694552):
rsync rsync://m1694552@dataserv.ub.tum.de/m1694552/
Sprache:
en
Rechte:
by, http://creativecommons.org/licenses/by/4.0
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