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)