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

Abstract

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 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 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 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 multi- path 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 appeals to intuition. We assess the performance of the new algorithm by clustering real-world measurement data from an indoor big hall environment.