Spatial decomposition of MIMO wireless channels (original) (raw)
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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.
Modeling and capacity of realistic spatial MIMO channels
2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221)
Accurate and tractable channel modeling is critical to realizing the full potential of antenna arrays in wireless communications. In this paper we propose a framework for modeling multi-antenna multipath channels based on the notion of virtual spatial angles. The virtual angles are fixed a priori and are determined by the number of antennas at the transmitter and receiver and the spacing between the antennas. The model essentially corresponds to a coordinate transformation via fixed spatial basis functions at the transmitter and receiver. The resulting linear virtual channel representation encompasses all existing models and provides a natural link between the. physical propagation environment and the channel statistics induced by it. For any given scattering environment, the model facilitates realistic estimates of channel capacity and clearly reveals the two key parameters affecting capacity: the number of parallel channels and the level of diversity.
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.
Directional Random Scattering MIMO Channels: Entropy Analysis and Capacity Optimization
2006 IEEE International Conference on Communications, 2006
In this paper, we study the effect of directional random scattering on the capacity of multiple-input multiple-output (MIMO) systems. First, we use the spatial decomposition of the MIMO channel matrix to analyze the randomness (entropy) of directional scattering. The analysis shows that directional scatterers (with at least a null in the angular power spectrum) will no longer be random when the receiver observation radius is sufficiently large. Therefore, directional scattering limits the expected linear increase of MIMO capacity with increasing the number of antennas. Second, we consider the effect of receiver antenna arrangement (positions) on the capacity of MIMO systems. For any random scatterer with a given angular power spectrum, we show that it is possible to choose the receiver antenna arrangement with the optimum whitening of the MIMO channel matrix that, in turn, maximizes MIMO channel capacity. I. INTRODUCTION A. Motivation and Background The pioneering work of Telatar [1], and Foschini and Gans [2] predicted linear capacity enhancement in multiple-input multiple-output (MIMO) systems by increasing the number of transmitter/receiver antenna elements. However, this analysis assumes independent fading paths between the transmitter and receiver antenna elements. In practical systems, the assumption of independent fading may be violated due to insufficient antenna spacing and restricted/directional angular spread of scatterers [2]. The effect of fading correlation on MIMO capacity has been studied in the literature, where channel correlation is often decomposed into the transmitter/receiver correlation matrices [3]-[7]. A limitation of this approach is that the effects of random scattering environment and deterministic antenna configurations are intertwined in the correlation matrices [8]. This often hinders obtaining a generic insight into the effect of directional scattering on the capacity of MIMO systems and the possibility of MIMO capacity optimization by using the optimum antenna configuration. Accurate physical modeling of the MIMO channel matrix requires that the scattering characteristics of the wireless spatial channel be taken into account. In [9], Muller proposed the decomposition of the MIMO channel based on the random propagation from transmitter antennas to the scattering objects
MIMO Channel Correlation in General Scattering Environments
2006 Australian Communications Theory Workshop
This paper presents an analytical model for the fading channel correlation in general scattering environments. In contrast to the existing correlation models, our new approach treats the scattering environment as non-separable and it is modeled using a bi-angular power distribution. The bi-angular power distribution is parameterized by the mean departure and arrival angles, angular spreads of the univariate angular power distributions at the transmitter and receiver apertures, and a third parameter, the covariance between transmit and receive angles which captures the statistical interdependency between angular power distributions at the transmitter and receiver apertures. When this third parameter is zero, this new model reduces to the well known "Kronecker" model. Using the proposed model, we show that Kronecker model is a good approximation to the actual channel when the scattering channel consists of a single scattering cluster. In the presence of multiple remote scattering clusters we show that Kronecker model over estimates the performance by artificially increasing the number of multipaths in the channel. I. INTRODUCTION Wireless channel modelling has received much attention in recent years since space-time processing using multiple antennas is becoming one of the most promising areas for improvements in performance of mobile communication systems [1], [2]. In channel modelling research, the effects of fading channel correlation due to insufficient antenna spacing and sparse scattering environments are of primary concern as they impact the performance of multiple-input multiple-output (MIMO) communication systems. A popular channel model that has been used in MIMO performance analysis is the "Kronecker" model [3]-[5]. In this model, the correlation properties of the MIMO channel are modeled at the transmitter and receiver separately, neglecting the statistical interdependency between scattering distributions at the transmitter and receiver antenna apertures. Measurement and analytical results presented in [6], [7] suggest that the Kronecker model does not accurately model the underlying scattering channel, therefore it does not provide accurate performance results. In this paper, using a recently proposed spatial channel model [8], we develop an alternate to the Kronecker model
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.
On Geometry-Based Statistical Channel Models for MIMO Wireless Communications
The use of wideband Multiple Input Multiple Output (MIMO) communication systems is currently subject to considerable interest. One reason for this is the latest development of 3rd Generation mobile communication systems and beyond, such as the wideband technology: Wideband Code Division Multiple Access (WCDMA), which provides 5 MHz wide radio channels. For the design and simulation of these mobile radio systems taking into account MIMO wireless propagation (e.g. like the wideband-CDMA), channel models are needed that provide the required spatial and temporal information necessary for studying such systems, i.e., the basic modeling parameters in the space-time domains, e.g., the root mean square (rms) delay spread (DS) is directly connected to the capacity of a specific communication system and gives a rough implication on the complexity of a receiver. In this thesis a channel modeling based on the clustering approach is proposed and used for analysis in the space-time domains for st...
2002
Channel capacity of multi-element communication systems in independent Rayleigh channels has been shown to scale linearly with the number of antennas. In reality, the signals received by different receiver antennas can be correlated with each other due to the non-uniform scattering environment and limited aperture of the antenna system. In this paper, the effect of spatial correlation between receiver antennas on capacity is investigated for various scattering environments. The physics of signal propagation is combined with statistics of the scattering environment to derive a capacity expression in terms of spatial correlation, antenna spacings/placement, aperture size, and power distribution of scatters. This result is used to show that for a given aperture size, one can increase the capacity approximately linearly up to a certain value by increasing the number of antennas but further increase will not give any significant capacity gain.
Models for MIMO propagation channels: a review
2002
This paper reviews recently published results on multiple input multiple output (MIMO) channel modeling. Both narrowband and wideband models are considered. We distinguish between two main approaches to MIMO channel modeling, namely, physically based and non-physically based modeling. The non-physical models primarily rely on the statistical characteristics of the MIMO channels obtained from the measured data while the physical models describe the MIMO channel (or its distribution) via some physical parameters. We briefly review different MIMO channel models and discuss their relationships. Some interesting aspects will be described in more detail and we note areas where few results are available.