[show abstract][hide abstract] ABSTRACT: The next challenge for MIMO radio channel models is to simulate the time-variant nature of the channel correctly. Cluster-based MIMO channel models are well suited for this problem, however they currently lack an accurate parameterization of the time-variant cluster parameters. In this paper we identify and track clusters from three different measurements conducted in an indoor, a sub-urban, and a rural environment. The time-variant cluster parameters of interest are: (i) cluster movement, (ii) change of cluster spreads, (iii) cluster lifetimes, and birth and death rates of cluster. We find that clusters show significant movement in parameter space depending on the environment. The spreads of individual clusters change rather randomly over their lifetime, with a standard deviation up to 150% of their mean spread. The cluster lifetime is approximately exponentially distributed, however additionally one has to account for long-living clusters coming from the line-of-sight path or from major reflectors.
Antennas and Propagation, 2007. EuCAP 2007. The Second European Conference on; 12/2007
[show abstract][hide abstract] ABSTRACT: 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 2.55 GHz and outdoor measurements at 300 MHz. The results from our joint clustering-and-tracking algorithm provide a good match with the physical propagation mechanisms observed in the measured scenarios.
Communications and Networking in China, 2007. CHINACOM '07. Second International Conference on; 09/2007