Clustering in wireless propagation channel with a statistics-based framework

In this article, we introduce a statistics-based clustering framework that is able to model the clustering problem corresponding to the channel propagation characteristics and evaluate the clustering results effectively. In the framework a Gaussian mixture model (GMM) is employed to model the channel multipaths. Then, we optimize the GMM parameters with the expectation-maximization (EM) algorithm. To evaluate the clustering results effectively, a compact index (CI) is devised, in which both the mean and variance of the clusters are considered. In the simulation, outdoor-to-indoor (O2I) channel measurement data is presented to demonstrate the effectiveness of the proposed framework.

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