Modeling and capacity of realistic spatial MIMO channels (original) (raw)

Experimental study of MIMO channel statistics and capacity via the virtual channel representation

2006

This work presents an experimental study of MIMO channel statistics and capacity via the recently proposed virtual channel representation, which describes the channel by a finite number of fixed virtual transmit/receive angles and delays. Our results confirm two important implications of virtual path partitioning: the virtual coefficients are approximately uncorrelated, and there exist fundamental angle-delay dependencies which limit the degrees of freedom in MIMO channels. The virtual channel power matrix reflects the distribution of channel power in angle-delay domain, quantifies the spatial-frequency correlation of actual channel coefficients, and also provides an intuitive explanation of the impact of scattering environments on capacity. The modeling accuracy of the popular Kronecker model, as well as the recently proposed eigenbeam model, is compared to the virtual channel model. The results indicate that both the virtual and eigenbeam models achieve good prediction accuracy, because they can model nonseparable 2D angular spectrum, while the Kronecker model results in larger prediction error due to the restrictive structure.

A virtual MIMO channel representation and applications

IEEE Military Communications Conference, 2003. MILCOM 2003.

A key to maximal exploitation of MIMO (multiple-input multiple-output) systems is a fundamental understanding of the interaction between the underlying complexphysical scattering envimnment and the space-time signal space. In time-andfrequency-selective MIMO (space-time) channels, this interaction happens in time, frequency and space. We present a four-dimensional Karhunen-Loeve-like virtual representarion for space-time channels that captures such interaction and exposes the intrinsic degrees offreedom in the channel. The virtual representation is a Fourier series dictated by thefinite array apertures, signaling duration and bandwidth and corresponds to a uniform, fixed sampling of the angle-delay-Doppler scattering space. It provides a much-needed connection between the two existing (extreme) modeling approachesidealized statistical models and detailedphysical (ray tracing} models. In particular: it yields a simple geometric interpretation of the effects ofphysical scattering on channel statistics and capacity. We discuss various insights into the structure of space-time channels afforded by the virtual representation as well its application in capacity assessment, spatial multiplexing and space-time code design.

Spatial Characterization of Multiple Antenna Channels

Multimedia Systems and Applications Series

In this chapter we present a realistic new model for wireless multipleinput multiple-output (MIMO) channels which is more general than previous models. A novel spatial decomposition of the channel is developed to provide insights into the spatial aspects of multiple antenna communication systems. By exploiting the underlying physics of free-space wave propagation we characterize the fundamental communication modes of a physical aperture and develop an intrinsic capacity which is independent of antenna array geometries and array signal processing. We show there exists a maximum achievable capacity for communication between spatial regions of space, which depends on the size of the regions and the statistics of the scattering environment.

Spatial correlation in wireless space-time MIMO channels

2007 Australasian Telecommunication Networks and Applications Conference, ATNAC 2007, 2008

In this contribution we focus on two principle methods of modelling MIMO radio channel, including the propagation-based and analytical method. In the propagationbased method, we present a space-time geometrical channel model with hyperbolically distributed scatterers for a macrocell mobile environment. On the other hand, popular mathematical models have been proposed to model the MIMO channel matrix include (i) the Kronecker model (ii) the Virtual Channel Representation Model and (iii) the Weichselberger Model. These models capture physical wave propagation and antenna configuration at both ends representing in a matrix form. This paper compares different analytical models that impose a particular structure on the MIMO channel matrix. The aim of using these models is to reduce the large number of parameters that can be used directly from the full correlation matrix. Furthermore, Four different antenna geometries are considered under different channel environment scenarios, namely uniform linear array, uniform circular array, Hexagon array and star array.

Spatial decomposition of MIMO wireless channels

Seventh International Symposium on Signal Processing and Its Applications, 2003. Proceedings., 2003

In this paper a novel decomposition of spatial channels is developed to provide insight into spatial aspects of multiple antenna communication systems. The underlying physics of the free space propagation is used to model the channel in scatterer free regions around the transmitter and the receiver, and the rest of the complex scattering media is represented by a parametric model. The channel matrix is separated into a product of known and random matrices where the known portion shows the effects of the physical configuration of antenna elements. We use the model to show the intrinsic degrees of freedom in a multi-antenna system. Potential applications of the model are briefly discussed.

STATISTICAL MODELLING OF SPATIALLY CORRELATED MIMO CHANNELS

A general statistical model for correlated MIMO channels is described. It will be valid for all kinds of algorithms using multiple antennas. The channel correlations are derived for the case of a flat fading environment. It is shown, that these correlations can be created in a statistical model by simply multiplying spatially uncorrelated channel matrices with the square roots of two covariance matrices. The result is generalized to the frequency selective case. In the following this model is used in Monte-Carlo simulations to compute MIMO capacities depending on the angular spread and the element spacing at the receive and transmit antenna array.

MIMO Capacity Analysis For Spatial Channel Model Scenarios

With the advancement of wireless communication and extensive research for new technologies for more reliable, secure and high speed connection, MIMO is coming up as the foremost competitor. In this research, we investigate MIMO system capacity using Spatial Channel Model (SCM) proposed by 3GPP-3GPP2 standard for the third generation systems and compare to one major physical model i.e. the One Ring Model. Also, we contrast with a theoretical model namely independent and identically distributed (i.i.d.) model. We present a system model for investigating MIMO systems followed by detailed analysis of channel parameters and capacity analysis. A simulation tool is developed to evaluate the capacity of N-LOS MIMO systems in SCM with scenario of multipath propagation. Further, it is compared with i.i.d. and One Ring model. The study shows that the channel capacity increases in almost linear fashion with addition of number of antennas, but the rate of linearity is higher in Waterfilling schemes and comparatively lower in Equal Power schemes. For practical implementation, the compact MIMO systems are more desirable, so we investigate the effect of mutual coupling due to closely spaced antennas. This study shows that mutual coupling leads to increase in the capacity for which the spacing is less than approximately 0.4 λ.

Survey of Channel and Radio Propagation Models for Wireless MIMO Systems

EURASIP Journal on Wireless Communications and Networking, 2007

This paper provides an overview of state-of-the-art radio propagation and channel models for wireless multiple-input multiple-output (MIMO) systems. We distinguish between physical models and analytical models and discuss popular examples from both model types. Physical models focus on the double-directional propagation mechanisms between the location of transmitter and receiver without taking the antenna configuration into account.

Modelling, Simulation and Capacity Analysis of Spatial Channel Models in MIMO System

Future wireless communication systems will utilize the spatial properties of the wireless channel to improve the spectral efficiency and thus increase capacity. This is realized by deploying multiple antennas at both the transmitter and receiver. Due to the unpredictable nature of the wireless channel, a common approach is to model its effects statistically. A few large world-wide co operations, like the third generation partnership project (3GPP) have developed channel models intended for reference and standardization use. These models are partly based on some bulk parameters that describe the characteristics of the channel over larger areas of several wavelengths. Such parameters include shadow fading, angle spread, and delay spread, etc. In the spatial channel model (SCM) these large-scale parameters are assumed independently between separate links, i.e., channel modelling, propagation between different mobile and base stations. This paper focuses on investigation of MIMO system capacity using the Spatial Channel Model (SCM) and Channel Capacity, Spatial Autocorrelation for different channel environments, proposed by standardization bodies (3GPP-3GPP2) for third generation systems. This SCM offers three environments such as suburban macro-cell, urban macro-cell and urban micro-cell parameters are obtained by using MATLAB 7.12.0.

Overview of spatial channel models for antenna array communication systems

IEEE Personal Communications, 1998

Spatial antenna diversity has been important in improving the radio link between wireless users. Historically, microscopic antenna diversity has been used to reduce the fading seen by a radio receiver, whereas macroscopic diversity provides multiple listening posts to ensure that mobile communication links remain intact over a wide geographic area. In later years, the concepts of spatial diversity have been expanded to build foundations for emerging technologies, such as smart (adaptive) antennas and position location systems. Smart antennas hold great promise for increasing the capacity of wireless communications because they radiate and receive energy only in the intended directions, thereby greatly reducing interference. To properly design, analyze, and implement smart antennas and to exploit spatial processing in emerging wireless systems, accurate radio channel models that incorporate spatial characteristics are necessary. In this tutorial, we review the key concepts in spatial...