Tracking time-variant cluster parameters in MIMO channel measurements: algorithm and results

• This paper presents a joint clustering-and-tracking framework to identify time-variant cluster parametersfor geometry-based stochastic MIMO channel models.The method uses a Kalman filter for tracking and predicting cluster positions, a novel consistentinitial guess procedure that accounts for predicted cluster centroids, and the well-known KPower-Means algorithm for cluster identification.We tested the framework by applying it to three entirely different sets of MIMO channel measurementdata obtained by different channel sounders: indoor measurements conducted at 2.55GHz, outdoor rural measurements at 300 MHz, and outdoor sub-urban measurements at 2.0 GHz.The time-variant cluster parameters of interest are: (i) cluster movement, (ii) change of clusterspreads, (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 astandard deviation up to 150% of their mean spread. The cluster lifetime is approximately exponentiallydistributed, however additionally one has to account for long-living clusters coming fromthe line-of-sight path or from major reflectors.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)

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