Models for MIMO propagation channels: a review

Yu, Kai; Ottersten, Björn

doi:10.1002/wcm.78

Cited by 208 publications

(93 citation statements)

References 36 publications

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“…Within the deterministic models we have the recorded impulse response and the ray tracing techniques. On the other hand, stochastic models which aim at reproducing observed phenomena by statistical means can be principally implemented as geometrically-based and correlation-based models [7].…”

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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Section: Fig1 a Mimo Communication Systemmentioning

confidence: 99%

Mimo Communication System At A Glance

Divya¹

2012

IOSRJECE

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“…The most general upper bounds of capacity of a MIMO system have been given in (15) and (16). Now these general upper bounds will be applied to MIMO system in free space.…”

Section: Upper Bounds Of Information Capacitymentioning

confidence: 99%

“…A shortcoming of these statistical descriptions for a wireless MIMO channel is that they do not explicitly contain the information about the physical parameters, such as antenna gain. In addition, these statistical descriptions depend on the measuring equipments, and the influences of the channel and equipments on the channel model cannot be separated [15]. Therefore the channel models that include physical parameters are preferred.…”

Section: Introductionmentioning

confidence: 99%

Multi-Antenna Information Theory

Geyi¹

2007

PIER

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Abstract-It is well known that the performance of a wireless multiple-input and multiple-output (MIMO) system depends on the propagation channel. The propagation channel models can generally be divided into two different groups, the statistical models based on information theory and the site-specific models based on measurement or ray tracing. In this paper, a general procedure for predicting the MIMO channel model has been presented. Analytical expressions for the channel matrix elements in a general scattering environment have been derived from the statistical theory for a narrow-band electromagnetic field, and have been verified by numerical simulation and experiments. The limitations of information capacity of the MIMO wireless communication system imposed by the antennas have also been discussed, and analytical upper bounds on the information capacity in terms of the antenna parameters for multiple antenna system in free space have been obtained. Once the capacity of a MIMO system is specified, these upper bounds can serve as a criterion for estimating how many antennas are needed or how big the antenna must be to achieve the capacity.

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“…Most of the available studies model the MIMO channels in a two-dimensional space [10]- [12]. A two-dimensional model with single-ring or dual-ring is proposed in [11].…”

Section: Introductionmentioning

confidence: 99%

A 3D Geometry-based Stochastic Model for 5G Massive MIMO Channels

Xie¹,

Li²,

Zuo³

et al. 2015

Proceedings of the 11th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustn

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Abstract-Massive MIMO is one of the most promising technologies for the fifth generation (5G) mobile communication systems. In order to better assess the system performance, it is essential to build a corresponding channel model accurately. In this paper, a three-dimension (3D) two-cylinder regular-shaped geometry-based stochastic model (GBSM) for non-isotropic scattering massive MIMO channels is proposed. Based on geometric method, all the scatters are distributed on the surface of a cylinder as equivalent scatters. Non-stationary property is that one antenna has its own visible area of scatters by using a virtual sphere. The proposed channel model is evaluated by comparing with the 3GPP 3D channel model [1]. The statistical properties are investigated. Simulation results show that close agreements are achieved between the characteristics of the proposed channel model and those of the 3GPP channel model, which justify the correctness of the proposed model. The model has advantages such as good applicability.

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Models for MIMO propagation channels: a review

Cited by 208 publications

References 36 publications

Mimo Communication System At A Glance

Mimo Communication System At A Glance

Multi-Antenna Information Theory

A 3D Geometry-based Stochastic Model for 5G Massive MIMO Channels

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