2nd International ICST Conference on Communications and Networking in China

Research Article

Tracking Time-Variant Cluster Parameters in MIMO Channel Measurements

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1109/CHINACOM.2007.4469589,
    author={Nicolai Czink and Ruiyuan Tian and Shurjeel Wyne and Fredrik Tufvesson and Jukka-Pekka Nuutinen and Juha Ylitalo and Ernst Bonek and Andreas F. Molisch},
    title={Tracking Time-Variant Cluster Parameters in MIMO Channel Measurements},
    proceedings={2nd International ICST Conference on Communications and Networking in China},
    publisher={IEEE},
    proceedings_a={CHINACOM},
    year={2008},
    month={3},
    keywords={Acoustic scattering  Azimuth  Clustering algorithms  Costs  Information technology  Joints  MIMO  Solid modeling  Stochastic processes  Testing},
    doi={10.1109/CHINACOM.2007.4469589}
}
  • Nicolai Czink
    Ruiyuan Tian
    Shurjeel Wyne
    Fredrik Tufvesson
    Jukka-Pekka Nuutinen
    Juha Ylitalo
    Ernst Bonek
    Andreas F. Molisch
    Year: 2008
    Tracking Time-Variant Cluster Parameters in MIMO Channel Measurements
    CHINACOM
    IEEE
    DOI: 10.1109/CHINACOM.2007.4469589
Nicolai Czink1,2, Ruiyuan Tian2, Shurjeel Wyne3, Fredrik Tufvesson3, Jukka-Pekka Nuutinen4, Juha Ylitalo4, Ernst Bonek1, Andreas F. Molisch3
  • 1: Institut fur Nachrichtentechnik und Hochfrequenztechnik, Technische Universitat Wien, Vienna, Austria
  • 2: Forschungszentrum Telekommunikation Wien (ftw.), Vienna, Austria
  • 3: Department of Electrical and Information Technology, Lund University, Lund, Sweden
  • 4: Elektrobit, Finland

Abstract

This paper presents a joint clustering-and-tracking framework to identify time-variant cluster parameters for geometry-based stochastic MIMO channel models. The method uses a Kalman filter for tracking and predicting cluster positions, a novel consistent initial guess procedure that accounts for predicted cluster centroids, and the well-known KPowerMeans algorithm for cluster identification. We tested the framework by applying it to two different sets of MIMO channel measurement data, indoor measurements conducted at 2.55 GHz and outdoor measurements at 300 MHz. The results from our joint clustering-and-tracking algorithm provide a good match with the physical propagation mechanisms observed in the measured scenarios.

Keywords
Acoustic scattering Azimuth Clustering algorithms Costs Information technology Joints MIMO Solid modeling Stochastic processes Testing
Published
2008-03-07
Publisher
IEEE
Modified
2011-07-19
http://dx.doi.org/10.1109/CHINACOM.2007.4469589
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