A distributed massive MIMO channel sounder for acquiring large CSI datasets, dubbed DICHASUS, is presented. The measured data has potential applications in the study of different machine learning algorithms for user localization, JCAS, channel charting, enabling massive MIMO in FDD operation, and many others. The proposed channel sounder architecture is distinct from similar previous designs in that each individual single-antenna receiver is completely autonomous, enabling arbitrary grouping into spatially distributed antenna deployments, and offering virtually unlimited scalability in the number of antennas. Optionally, extracted channel coefficient vectors can be tagged with ground truth position data, obtained either through a GNSS receiver (for outdoor operation) or through various indoor positioning techniques.
%0 Conference Paper
%1 euchner2021distributed
%A Euchner, Florian
%A Gauger, Marc
%A Dörner, Sebastian
%A ten Brink, Stephan
%B 25th International ITG Workshop on Smart Antennas (WSA 2021)
%D 2021
%K myown sounder from:sdnr distributed channel csi mimo ml
%T A Distributed Massive MIMO Channel Sounder for "Big CSI Data"-driven Machine Learning
%U https://ieeexplore.ieee.org/document/9739175
%X A distributed massive MIMO channel sounder for acquiring large CSI datasets, dubbed DICHASUS, is presented. The measured data has potential applications in the study of different machine learning algorithms for user localization, JCAS, channel charting, enabling massive MIMO in FDD operation, and many others. The proposed channel sounder architecture is distinct from similar previous designs in that each individual single-antenna receiver is completely autonomous, enabling arbitrary grouping into spatially distributed antenna deployments, and offering virtually unlimited scalability in the number of antennas. Optionally, extracted channel coefficient vectors can be tagged with ground truth position data, obtained either through a GNSS receiver (for outdoor operation) or through various indoor positioning techniques.
@inproceedings{euchner2021distributed,
abstract = {A distributed massive MIMO channel sounder for acquiring large CSI datasets, dubbed DICHASUS, is presented. The measured data has potential applications in the study of different machine learning algorithms for user localization, JCAS, channel charting, enabling massive MIMO in FDD operation, and many others. The proposed channel sounder architecture is distinct from similar previous designs in that each individual single-antenna receiver is completely autonomous, enabling arbitrary grouping into spatially distributed antenna deployments, and offering virtually unlimited scalability in the number of antennas. Optionally, extracted channel coefficient vectors can be tagged with ground truth position data, obtained either through a GNSS receiver (for outdoor operation) or through various indoor positioning techniques.},
added-at = {2022-04-26T16:12:58.000+0200},
author = {Euchner, Florian and Gauger, Marc and Dörner, Sebastian and ten Brink, Stephan},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2fd8ae4ab782e3b470967adc34723afc0/inue},
booktitle = {25th International ITG Workshop on Smart Antennas (WSA 2021)},
interhash = {ad997828ebffcf60d901bce85a6286d2},
intrahash = {fd8ae4ab782e3b470967adc34723afc0},
keywords = {myown sounder from:sdnr distributed channel csi mimo ml},
timestamp = {2022-04-26T14:12:58.000+0200},
title = {A Distributed Massive MIMO Channel Sounder for "Big CSI Data"-driven Machine Learning},
url = {https://ieeexplore.ieee.org/document/9739175},
venue = {Sophia Antipolis, France},
year = 2021
}
