A flexibly configurable spatial model for mobile radio channels

IEEE Trans. Wireless Commun.

Clerckx

Vanhoenacker‐Janvier

et al. 2005

This paper highlights the impact of channel correlations on the capacity and performance of MIMO communications, with a focus on the so-called diagonal correlations. Based on this analysis, the limitations of simplified mathematical representations, such as the Kronecker or the diagonal-decorrelation models, are pointed out. Finally, the correlation properties of popular geometry-based statistical models are studied in order to analyze whether the correlation structure of these models can be adequately represented by simplified mathematical models, as well as to quantify the errors introduced by these simplifications. With respect to channel correlations, neither the Kronecker nor the diagonal-decorrelation assumptions are good representations of the correlation structure of the investigated geometry-based statistical models. When comparing capacity and symbol error probability results, it is found that the diagonal-decorrelation model may yield significant errors on both considered metrics. The Kronecker model generally yields errors less than one order of magnitude on the symbol error rate, but relative errors on ergodic or outage capacity may be more significant.

Section: A Mimo Channelsmentioning

confidence: 99%

Impact of fading correlations on MIMO communication systems in geometry-based statistical channel models

IEEE Trans. Wireless Commun.

Clerckx

Vanhoenacker‐Janvier

et al. 2005

“…The power-angle profiles at the BTS and the CPE are obtained by (4) (5) In two-dimensional models, the only angular variable is the azimuth, so and represent the usual power azimuth spectra.…”

Section: A Channel Impulse Responsementioning

confidence: 99%

“…A number of existing statistical models [1]- [5] are only valid for Rayleigh microcellular channels, so the existence of a dominant path cannot be accounted for. Moreover, these models often fail to address the range and antenna beamwidth dependencies of the channel properties.…”

mentioning

confidence: 99%

Range and Antenna Beamwidth Dependencies in Multidimensional Fixed Wireless Channels

IEEE Trans. Wireless Commun.

Paulraj²

2004

This paper describes a multidimensional model of the fixed wireless propagation channel, which is well suited to systemlevel simulations. The proposed method yields realistic predictions of various channel characteristic parameters as a function of the range and antenna beamwidths, in agreement with experimentally observed results. The originality of the approach is that the powerdelay profile for omnidirectional antennas at the edge of the cell is used to predict the time-varying channel over the whole cell for any antenna beamwidth. The method is based on a set of spatially distributed scatterers, which can be scaled to any range within the cell. The time-varying channel impulse response is then calculated as the combination of all scattered contributions by means of a ray approximation. The multidimensionality of the channel model is explored through predictions of signal statistics, level-crossing rate, delay-spread and angle-spread. In contrast to most existing models, the impact of range and antenna beamwidth is clearly addressed and found to be close to experimentally observed behaviors.

“…The location of any point is a vector denoted as . Relative vectors are obtained by (11) where and may represent the BTS/CPE antenna phase center (denoted as and ); the BTS (transmit) antenna (denoted as , ), the CPE (receive) antenna (denoted as , ), and the scatterer on the ellipse (denoted as ). The channel matrix is calculated using a ray-based approach, similar to geometrical optics.…”

Section: Mimo Channel Formulationmentioning

confidence: 99%

“…Each cluster is characterized by a uniformly distributed mean angle of arrival; angular spread is accounted for by means of a Laplacian distribution [10]. A similar single-scattering model [11] is based on different environment types, for which a different scatterer distribution is defined. A stochastic model is also described in [12], including empirical input such as the power-delay spectrum and the spatial correlation functions.…”

mentioning

confidence: 99%

A physical scattering model for MIMO macrocellular broadband wireless channels

IEEE J. Select. Areas Commun.

Erceg²,

Paulraj³

2003

103

Abstract-This paper presents a physical scattering model that predicts multiple-input multiple-output (MIMO) channel characteristics conforming well to experimental observations in macrocells. Our approach is to start with a given single-input single-output power-delay profile (defined for specific range, bandwidth and antenna parameters) and fit a scattering model that characterizes the MIMO channel. From the derived scattering model and antenna array configurations, the MIMO channel is computed using a ray-based method. Simulations of several MIMO channels are shown to exhibit experimentally observed channel correlations, antenna beamwidth effect, range dependency, and frequency selectivity.Index Terms-Broadband communication, indexing terms multiple-input multiple-output (MIMO) systems, multipath channels, propagation, scattering.