This paper discusses over-the-air (OTA) test setup for multiple-input-multiple-output (MIMO) capable terminals with emphasis on channel modelling. The setup is composed of a fading emulator, an anechoic chamber, and multiple probes. Creation of a propagation environment inside an anechoic chamber requires unconventional radio channel modelling, namely, a specific mapping of the original models onto the probe antennas. We introduce two novel methods to generate fading emulator channel coefficients; the prefaded signals synthesis and the plane wave synthesis. To verify both methods we present a set of simulation results. We also show that the geometric description is a prerequisite for the original channel model.

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The problem of identifying clusters from MIMO measurement data is addressed. Conventionally, visual inspection has been used for cluster identification, but this approach is impractical for a large amount of measurement data. For automatic clustering, the multipath component distance (MCD) is used to calculate the distance between individual multipath components estimated by a channel parameter estimator, such as SAGE. This distance is implemented in the well-known KMeans clustering algorithm. To demonstrate the effectiveness of the choice made, the performance of the MCD and the Euclidean distance were compared by clustering synthetic data generated by the 3GPP spatial channel model (SCM). Using the MCD significantly improved clustering performance.

Recent findings suggest to split the impulse response of the radio channel into discrete paths and the "diffuse multipath" (DMP). This diffuse part can be described by an exponentially decaying power delay profile. This paper shows how to improve current radio channel models using the DMP concept. From MIMO channel measurements, we find that the DMP parameters are strongly correlated with the parameters of the discrete paths. This holds for various (indoor) environments. We provide a simple way to model the statistics of the DMP parameters.Finally, we include the DMP concept in a novel MIMO model, the Random-Cluster model. We find that including DMP significantly improves the model fit in terms of mutual information and channel diversity.