Abstract
This paper presents the Random-Cluster Model (RCM), a stochastic time-variant, frequency-selective, propagation-based MIMO channel model that is directly parametrised from measurements. Using a fully automated algorithm, multipath clusters are identified from measurement data without user intervention. The cluster parameters are then used to define the propagation environment in the RCM. In this way, the RCM provides a direct link between MIMO channel measurements and MIMO channel modelling. For validation, we take state-of-the-art MIMO measurements, and parametrise the RCM exemplarly. Using three different validation metrics, namely, mutual information, channel diversity, and the novel Environment Characterisation Metric, we find that the RCM is able to reflect the measured environment remarkably well.
Highlights
Multiple-input multiple-output technology (MIMO) [1] made its way in the recent years from an informationtheoretic shooting star [2] to actual products on the mass market [3, 4]
The cafeteria scenario is a challenging one, difficult to represent by any MIMO channel model, as it is a combination of two totally different propagation environments, depending on whether the LOS between Rx and Tx is blocked or not
For validation we generated smoothly-time varying channels using the Random-Cluster Model (RCM) and used the three validation metrics described in the previous paragraphs
Summary
Multiple-input multiple-output technology (MIMO) [1] made its way in the recent years from an informationtheoretic shooting star [2] to actual products on the mass market [3, 4]. These models try to predict MIMO conditions given a map (or floor plan) for optimal positioning of MIMO-enabled base stations, which comes with high demands on computational power and accuracy of environment data bases; (iii) channel models for testing of algorithms and systems, for example, [14–16, Chapter 6.8] These models typically represent a certain kind of propagation scenario (like indoor offices, EURASIP Journal on Wireless Communications and Networking or outdoor picocells), without considering a specific propagation environment. We present the novel Random-Cluster Model (RCM), a geometry-based stochastic MIMO channel model for time-variant frequency-selective channels. (A first description of the RCM, modelling random-access channels only was provided in [25], and [26] briefly outlines the ideas of using clusters for time-variant channel modelling.). In Appendix A, we provide an overview of the measurements used for parametrisation and validation
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