A Time-Variant MIMO Channel Model Directly Parametrised from Measurements

EURASIP Journal on Wireless Communications and Networking, Apr 2009

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.

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A Time-Variant MIMO Channel Model Directly Parametrised from Measurements

EURASIP Journal on Wireless Communications and Networking Hindawi Publishing Corporation A Time-Variant MIMO Channel Model Directly Parametrised from Measurements Nicolai Czink 1 2 Thomas Zemen 2 Jukka-Pekka Nuutinen 0 Juha Ylitalo 0 Ernst Bonek 3 0 Elektrobit Ltd. , 90570 Oulu , Finland 1 Smart Antennas Research Group, Stanford University , Stanford, CA 94305 , USA 2 Telecommunications Research Center Vienna (FTW) , 1220 Vienna , Austria 3 Institute of Communications and Radio Frequency Engineering, Vienna University of Technology , 1040 Vienna , Austria 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. 1. Introduction 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 ]. Currently the 3GPP [5] is standardising MIMO for the next generation’s mobile communications, what is called Long Term Evolution (LTE) as well as IEEE is standardising MIMO for WiMAX [ 6 ]. Already information theory told that the promise of increased spectral efficiency of MIMO systems is only available when the radio channel permits, but this seems to have faded out of people’s memory. Despite this fact, numerous algorithms were developed, mostly considering ideal uncorrelated i.i.d. Rayleigh fading channels between the transmit and receive antennas, which is only true in rich-scattering environments with sufficiently large antenna spacings at both transmitter and receiver. Otherwise, the performance of the algorithms deteriorates. To reach the goal of gigabit transmissions over the wireless link, one needs to include the knowledge of the actual channel into the algorithms. Thus, an accurate model of the propagation channel is paramount. One can distinguish between three different types of MIMO channel models: (i) channel models for developing signal-processing algorithms, for example, [ 7, 8 ]. These models describe the radio channel by the correlations between the different links, established between individual antenna elements. This makes the model mathematically tractable, yet inaccurate when it comes to reflecting real-world propagation conditions, because current correlation-based models always base on the Rayleigh-fading (or, to some extent, Ricean fading) assumption. While the so-called “Kronecker” model [7] is favoured by many people because it can be treated by random-matrix theory [ 9 ], the Weichselberger Model [ 8 ] shows a much better fit to measurement data [ 10, 11 ]. (ii) channel models for MIMO deployment in a given environment, for example, ray-tracing [ 12, 13 ]. 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, or outdoor picocells), without considering a specific propagation environment. This is achieved by modelling the propagation environment in a stochastic way. Such models usually have a medium complexity and represent realistic channels very well, however a closed-form expression of the channel model, as in the first case, does not exist. The major difference between these models is their ability to describe time variation. A time-variant channel is an essential feature of mobile communications. The 3GPP Spatial Channel Model (SCM) [ 14 ] is well suited for simulating random-access communications. It models the channel in blocks (so-called “drops”), during which the channel only undergoes Doppler fading, but after a drop, the channel changes completely. This assumption makes it impossible to test signal processing algorithms that track the channel parameters between different snapshots. Additionally, the abrupt changes between the drops are challenging for hardware testing using channel simulators, since the device under test and the channel model need to be synchronized. A major improvement is the WINNER II geometry-based stochastic channel model [ 1 (...truncated)


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Nicolai Czink, Thomas Zemen. A Time-Variant MIMO Channel Model Directly Parametrised from Measurements, EURASIP Journal on Wireless Communications and Networking, 2009, pp. 687238, Volume 2009, Issue 1, DOI: 10.1155/2009/687238