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...

• Type: Conference
• Year: 2006
• Where: VTC
• Authors: Nicolai Czink, Pierluigi Cera, Jari Salo, Ernst Bonek, Jukka-Pekka Nuutinen, Juha Ylitalo
• Keywords: Cluster Identification, Measurement Data, MIMO Measurement Data, VTC 2006