A stochastic multiple-input-multiple-output radio channel model for evaluation of space-time coding algorithms

Pedersen, Klaus I.; Andersen, Jørgen Bach; Kermoal, J.P.; Mogensen, Preben

doi:10.1109/vetecf.2000.887129

Cited by 179 publications

(122 citation statements)

References 23 publications

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“…In [29], a distributed scattering model has been reported in order to explain the pinhole phenomenon [30] that may appear in outdoor scenarios. A model based on channel power correlation coefficients has been presented in [31] and is extended to include the complex valued correlation matrix in [32]. For a detailed review on MIMO wireless channel models, see [33].…”

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confidence: 99%

Modeling of Wide-Band MIMO Radio Channels Based on NLoS Indoor Measurements

Bengtsson

Ottersten

et al. 2004

IEEE Trans. Veh. Technol.

242

137

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Abstract-In this paper, we first verify a previously proposed Kronecker-structure-based narrow-band model for nonline-ofsight (NLoS) indoor multiple-input-multiple-output (MIMO) radio channels based on 5.2-GHz indoor MIMO channel measurements. It is observed that, for the narrow-band case, the measured channel coefficients are complex Gaussian distributed and, consequently, we focus on a statistical description using the first-and second-order moments of MIMO radio channels. It is shown that the MIMO channel covariance matrix can be well approximated by the Kronecker product of the covariance matrices, seen from the transmitter and receiver, respectively. A narrow-band model for NLoS indoor MIMO channels is thus verified by these results. As for the wide-band case, it is observed that the average power-delay profile of each element of the channel impulse response matrix fits the exponential decay curve and that the Kronecker structure of the second-order moments can be extended to each channel tap. A wide-band MIMO channel model is then proposed, combining a simple COST 259 single-inputsingle-output channel model and the Kronecker structure. Monte Carlo simulations are used to generate indoor MIMO channel realizations according to the models discussed. The results are compared with the measured data using the channel capacity and good agreement is found.

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confidence: 99%

Modeling of Wide-Band MIMO Radio Channels Based on NLoS Indoor Measurements

Bengtsson

Ottersten

et al. 2004

IEEE Trans. Veh. Technol.

242

137

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“…However, in order to study the effect of MC on the channel capacity under specific propagation environment spatial correlation properties it is assumed that the inter-element spacing affects only the MC matrix but not the angular spread information. In order to account for the angular spread changes due to the propagation environment, the Kronecker stochastic MIMO radio channel model is used [18] with a specific spatial receiver correlation value. Fig.…”

Section: Numerical Resultsmentioning

confidence: 99%

Effect of Mutual Coupling on Capacity of Mimo Wireless Channels in High SNR Scenario

Abouda¹,

El-Sallabi²,

Häggman³

2006

PIER

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Abstract-Theoretical results on the effect of antenna mutual coupling (MC) on capacity of multiple-input multiple-output (MIMO) wireless channels are presented in this paper with particular emphasis on the case of high signal to noise ratio (SNR) scenario. Two cases are considered, 1-channel capacity variations due to MC effect on correlation properties and target average receive SNR and 2-channel capacity variations due to MC effect on correlation properties at fixed average receive SNR. It is shown that the effect of MC on MIMO channel capacity can be positive or negative depending on the propagation environment spatial correlation properties and the characteristics of the transmitter and receiver MC matrices. Conditions where MC has positive and negative effects on MIMO channel capacity in the two considered cases are identified. Numerical results for half wavelength dipole antenna supporting the theoretical observations are presented.

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“…Its popularity comes from the fact that this correlation based model appear to give more insight in the capacity increase of MIMO channels, since these are based on the channel covariance, establishing a direct link between the channel covariance matrix rank and the channel capacity [8].…”

Section: Fig1 a Mimo Communication Systemmentioning

confidence: 99%

Mimo Communication System At A Glance

Divya¹

2012

IOSRJECE

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ABSTRACT---MultipleThe fading channel corrupts the transmitted information symbols and adds noise to the signal through the channel, it is assumed that the signal fades according to the Rayleigh distribution which is applicable when there is no line of sight propagation between the transmitter and the receiver it is a reasonable model for tropospheric and ionospheric signal propagation as well as the effect of heavy build up urban environments on the radio signal. Also the received signal vector is a linear superposition of separately transmitted information symbols, and this poses a significant challenge to the user to extract the correct information from the received signal. The goal of the MIMO technology is to minimize the BER and simultaneously improve the data rates, this is achieved through the use of spatial multiplexing which aims to increase the data rates and Space Time Codes are used which employ transmit diversity in a optimal and systematic way by adding redundancy to minimize the effects of fading, noise and interference thereby reducing BER [4]. A MIMO system can simultaneously obtain both type of gains, but there is a fundamental tradeoff between how much of each type of gain any signaling scheme can extract [5]. Also the receivers employed must have the capability to extract the correct information from the received signal. The most commonly used detection techniques are Linear Detection, Successive interference cancellation, Maximum-Likelihood Detection (MLD) and Sphere Decoding (SD) [12], [13]. Another major challenge faced is to obtain the channel state information precisely. There are three methods for the channel estimation training based, blind and semi-blind channel estimation. [6]

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A stochastic multiple-input-multiple-output radio channel model for evaluation of space-time coding algorithms

Cited by 179 publications

References 23 publications

Modeling of Wide-Band MIMO Radio Channels Based on NLoS Indoor Measurements

Modeling of Wide-Band MIMO Radio Channels Based on NLoS Indoor Measurements

Effect of Mutual Coupling on Capacity of Mimo Wireless Channels in High SNR Scenario

Mimo Communication System At A Glance

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