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2006
IEEE

A Framework for Automatic Clustering of Parametric MIMO Channel Data Including Path Powers

8 years 11 months ago
A Framework for Automatic Clustering of Parametric MIMO Channel Data Including Path Powers
— We present a solution to the problem of identifying clusters from MIMO measurement data in a data window, with a minimum of user interaction. Conventionally, visual inspection has been used for the cluster identification. However this approach is impractical for a large amount of measurement data. Moreover, visual methods lack an accurate definition of a “cluster” itself. We introduce a framework that is able to cluster multi-path components (MPCs), decide on the number of clusters, and discard outliers. For clustering we use the K-means algorithm, which iteratively moves a number of cluster centroids through the data space to minimize the total difference between MPCs and their closest centroid. We significantly improve this algorithm by following changes: (i) as the distance metric we use the multipath component distance (MCD), (ii) the distances are weighted by the powers of the MPCs. The implications of these changes result in a definition of a “cluster” itself that...
Nicolai Czink, Pierluigi Cera, Jari Salo, Ernst Bo
Added 12 Jun 2010
Updated 12 Jun 2010
Type Conference
Year 2006
Where VTC
Authors Nicolai Czink, Pierluigi Cera, Jari Salo, Ernst Bonek, Jukka-Pekka Nuutinen, Juha Ylitalo
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