Spatial correlation in indoor MIMO channels

McNamara, D.; Beach, Mark A; Fletcher, P.N.

doi:10.1109/pimrc.2002.1046707

Cited by 57 publications

(34 citation statements)

References 7 publications

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“…These are common performance measures used to validate the performance of a channel model based on measured data. In addition, there is another less common performance measure specific for the Kronecker structure that was used in [3] and [6]. The model error that measures how good the full channel correlation matrix can be modeled as the Kronecker product of the two ends correlation matrices.…”

Section: Discussionmentioning

confidence: 99%

“…However, it has been widely noticed that this channel model has shown different performances under different propagation scenarios. While in [2,3] it is shown that the Kronecker model is very suitable for matching results from measured data with small number of transmit and receive antenna elements, in other studies [5,6] significant differences between the results from the measured data and the predicted results are reported when large number of transmit and receive antennas is used. Although, the simplicity of the Kronecker model makes it an attractive starting point in the analysis of any space-time processing technique.…”

Section: Introductionmentioning

confidence: 99%

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Spatial Smoothing Effect on Kronecker Mimo Channel Model in Urban Microcells

Abouda¹,

El-Sallabi²,

Vuokko³

et al. 2007

Journal of Electromagnetic Waves and Applications

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Abstract-In this paper, the effect of spatial smoothing on performance of widely used stochastic narrowband multiple-input multiple-output (MIMO) radio channel model, namely the Kronecker model, is investigated based on data measured in urban microcellular environment at 5.3 GHz carrier frequency. Results from non-line of sight (NLOS) and line of sight (LOS) traveling routes are analyzed. It is noticed that in NLOS traveling route spatial smoothing improves the accuracy of the large eigenvalues of the channel correlation matrix and significantly enhances the applicability of the Kronecker structure. Under LOS condition both the Kronecker model and its smoothed version fail to render the eigenvalues of the measured channel correlation matrix but spatial smoothing slightly improves the applicability of the Kronecker structure.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatial Smoothing Effect on Kronecker Mimo Channel Model in Urban Microcells

Abouda¹,

El-Sallabi²,

Vuokko³

et al. 2007

Journal of Electromagnetic Waves and Applications

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“…Measurement campaigns have shown that the Kronecker product correlation model is accurate in capturing the underlying channel statistics under certain conditions [10], but in general, the model underestimates the measured channel capacity [8] and other system parameters.…”

Section: Channel Modelsmentioning

confidence: 99%

When is limited feedback for transmit beamforming beneficial?

Raghavan

Sayeed

Boston

2005

Proceedings. International Symposium on Information Theory, 2005. ISIT 2005.

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Multi-antenna systems have been shown to offer tremendous capacity increase in ideal rich scattering conditions. Transmit beamforming and receive combining are low complexity techniques that help in achieving the full diversity afforded by the multiantenna channel. Even if complete channel knowledge at the receiver maybe a realistic assumption in these systems, the same may not be true at the transmitter side. Thus, quantized beamforming with limited feedback on a reverse link has been a topic that has attracted great attention recently. But almost all of the work to date has focussed on modeling the channel with independent, identically distributed (i.i.d.) Rayleigh fading between antenna pairs. In this report, we first review the earlier works which focus on Grassmannian line packing. We then consider the correlated channel case, and show that Grassmannian line packing is an artificial artifact of the i.i.d. assumption. We show that there are dominant peaks in the eigen-domain when correlation is imposed and the code-book construction should be matched to the correlation in the channel, which renders the problem in this case easier than for the i.i.d. channels.

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“…Multiple-input multiple-output (MIMO) techniques stand as a strong candidate to allow robustness against channel fading and interference as well as to enable high data rates [1,2]. However, the performance of future MIMO wireless communication systems strongly depends on the propagation environment and the antenna array configuration [3] [4]. Previous research results have focused largely on evaluating MIMO system performance under the assumption of uniform linear array (ULA) geometry at both ends with a specific array orientation.…”

Section: Introductionmentioning

confidence: 99%

Effect of Antenna Array Geometry and Ula Azimuthal Orientation on Mimo Channel Properties in Urban City Street Grid

Abouda¹,

El-Sallabi²

2006

PIER

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Abstract-Propagation environment and antenna array configuration have significant effect on spatial correlation properties of multipleinput multiple-output (MIMO) wireless communications channels. In this paper the effect of different antenna array geometries on MIMO channel properties is investigated in urban city street grid propagation environment. Four antenna array geometries with the same number of antenna elements and fixed inter-element spacing are considered, namely, uniform linear array (ULA), uniform circular array (UCA), uniform rectangular array (URA) and uniform cubic array (UCuA). The effect of ULA orientation in azimuthal plane on MIMO channel ergodic capacity is also investigated. Varying orientation angle from 0 to π at the two communication ends is considered. The investigation is carried out based on three dimensional (3D) spatial multi-ray realistic propagation channel model covering different propagation types. It is shown that the antenna array geometry have significant impact on MIMO channel properties. Under different propagation scenarios the ULA shows superiority to the other considered geometries in terms of the ergodic channel capacity and number of spatial parallel channels. However, this superiority depends largely on the array azimuthal orientation.

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Spatial correlation in indoor MIMO channels

Cited by 57 publications

References 7 publications

Spatial Smoothing Effect on Kronecker Mimo Channel Model in Urban Microcells

Spatial Smoothing Effect on Kronecker Mimo Channel Model in Urban Microcells

When is limited feedback for transmit beamforming beneficial?

Effect of Antenna Array Geometry and Ula Azimuthal Orientation on Mimo Channel Properties in Urban City Street Grid

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