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 2.55GHz and outdoor measurements at 300MHz. The results from our joint clustering-and-tracking algorithm provide a good match with the physical propagation mechanisms observed in the measured scenarios.
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