Nicolai Czink

Survey of channel and radio propagation models for wireless MIMO systems

This paper provides an overview of the 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. Analytical models capture physical wave propagation and antenna configuration simultaneously by describing the impulse response (equivalently, the transfer function) between the antenna arrays at both link ends. We also review some MIMO models that are included in current standardization activities for the purpose of reproducible and comparable MIMO system evaluations. Finally, we describe a couple of key features of channels and radio propagation which are not sufficiently included in current MIMO models.

A geometry-based stochastic MIMO model for vehicle-to-vehicle communications

Vehicle-to-vehicle (VTV) wireless communications have many envisioned applications in traffic safety and congestion avoidance, but the development of suitable communications systems and standards requires accurate models for the VTV propagation channel. In this paper, we present a new wideband multiple-input-multiple-output (MIMO) model for VTV channels based on extensive MIMO channel measurements performed at 5.2 GHz in highway and rural environments in Lund, Sweden. The measured channel characteristics, in particular the nonstationarity of the channel statistics, motivate the use of a geometry-based stochastic channel model (GSCM) instead of the classical tapped-delay line model. We introduce generalizations of the generic GSCM approach and techniques for parameterizing it from measurements and find it suitable to distinguish between diffuse and discrete scattering contributions. The time-variant contribution from discrete scatterers is tracked over time and delay using a high resolution algorithm, and our observations motivate their power being modeled as a combination of a (deterministic) distance decay and a slowly varying stochastic process. The paper gives a full parameterization of the channel model and supplies an implementation recipe for simulations. The model is verified by comparison of MIMO antenna correlations derived from the channel model to those obtained directly from the measurements.

Vehicular Channel Characterization and Its Implications for Wireless System Design and Performance

To make transportation safer, more efficient, and less harmful to the environment, traffic telematics services are currently being intensely investigated and developed. Such services require dependable wireless vehicle-to-infrastructure and vehicle-to-vehicle communications providing robust connectivity at moderate data rates. The development of such dependable vehicular communication systems and standards requires accurate models of the propagation channel in all relevant environments and scenarios. Key characteristics of vehicular channels are shadowing by other vehicles, high Doppler shifts, and inherent nonstationarity. All have major impact on the data packet transmission reliability and latency. This paper provides an overview of the existing vehicular channel measurements in a variety of important environments, and the observed channel characteristics (such as delay spreads and Doppler spreads) therein. We briefly discuss the available vehicular channel models and their respective merits and deficiencies. Finally, we discuss the implications for wireless system design with a strong focus on IEEE 802.11p. On the road towards a dependable vehicular network, room for improvements in coverage, reliability, scalability, and delay are highlighted, calling for evolutionary improvements in the IEEE 802.11p standard. Multiple antennas at the onboard units and roadside units are recommended to exploit spatial diversity for increased diversity and reliability. Evolutionary improvements in the physical (PHY) and medium access control (MAC) layers are required to yield dependable systems. Extensive references are provided.

Path Loss Modeling for Vehicle-to-Vehicle Communications

Vehicle-to-vehicle (V2V) communications have received increasing attention lately, but there is a lack of reported results regarding important quantities such as path loss. This paper presents parameterized path loss models for V2V communications based on extensive sets of measurement data collected mainly under line-of-sight conditions in four different propagation environments: highway, rural, urban, and suburban. The results show that the path loss exponent is low for V2V communications, i.e., path loss slowly increases with increasing distance. We compare our results to those previously reported and find that, while they confirm some of the earlier work, there are also differences that motivate the need for further studies.

Cluster Characteristics in a MIMO Indoor Propagation Environment

Essential parameters of physical, propagation-based MIMO channel models are the fading statistics and the directional spread of multipath clusters. In this paper we determine these parameters in the azimuth-of-arrival/azimuth-of-departure (AoA/AoD) domain based on comprehensive indoor MIMO measurements at 5.2 GHz in a cluttered office environment using the SAGE algorithm for parameter estimation. Due to cluster identification in AoA/AoD-domain we found a greater number of clusters than those reported in previous publications. Regarding the fading statistics of clusters, so far not studied, strong (obstructed-)line-of-sight clusters show Rician fading, corresponding to few dominant propagation paths, whereas most clusters exhibit Rayleigh fading, corresponding to many paths with approximately equal powers and uncorrelated phases. Root-mean-square cluster azimuth spreads (CASs) were estimated with a novel method by appropriately restricting the support of the cluster azimuth distribution. We found that the estimated CASs are different when seen from transmitter or receiver, i.e. their ranges are from 2deg to 9deg and from 2deg to 7deg at the transmitter side and the receiver side, respectively

Correlation matrix distance, a meaningful measure for evaluation of non-stationary MIMO channels

The correlation matrix distance (CMD), an earlier introduced measure for characterization of non-stationary MIMO channels, is analyzed regarding its capability to predict performance degradation in MIMO transmission schemes. For that purpose we consider the performance reduction that a prefiltering MIMO transmission scheme faces due to non-stationary changes of the MIMO channel. We show that changes in the spatial structure of the channel corresponding to high values in the CMD also show up as a significant reduction in performance of the considered MIMO transmission scheme. Such significant changes in the spatial structure of the mobile radio channel are shown to appear also for small movements within an indoor environment. Stationarity can therefore not always be assumed for indoor MIMO radio channels.

Experimental Characterization and Modeling of Outdoor-to-Indoor and Indoor-to-Indoor Distributed Channels

We propose and parameterize an empirical model of the outdoor-to-indoor and indoor-to-indoor distributed (cooperative) radio channel, using experimental data in the 2.4-GHz band. In addition to the well-known physical effects of path loss, shadowing, and fading, we include several new aspects in our model that are specific to multiuser distributed channels: 1) correlated shadowing between different point-to-point links, which has a strong impact on cooperative system performance; 2) different types of indoor node mobility with respect to the transmitter and/or receiver nodes, implying a distinction between static and dynamic shadowing motivated by the measurement data; and 3) a small-scale fading distribution that captures more severe fading than that given by the Rayleigh distribution.

Car-to-car radio channel measurements at 5 GHz: Pathloss, power-delay profile, and delay-Doppler spectrum

We carried out a car-to-infrastructure (C2I) and car-to-car (C2C) 4x4 multiple-input multiple-output (MIMO) radio channel measurement campaign at 5.2 GHz in Lund, Sweden. This paper presents first results on pathloss, power-delay profiles, and delay-Doppler spectra in a C2C highway scenario, where both cars were traveling in opposite directions. A pathloss coefficient of 1.8 yields the best fit with our measurement results in the mean square sense. The measured Doppler shift of the line of sight path matches exactly with theoretical calculations. Selected paths are investigated in the delay and Doppler domain. The avererage delay spread is 250 ns; Doppler shifts of more than 1000 Hz are observed.

Non-WSSUS vehicular channel characterization in highway and urban scenarios at 5.2GHz using the local scattering function

The fading process in high speed vehicular traffic telematic applications at 5 GHz is expected to fulfill the wide-sense stationarity uncorrelated scattering (WSSUS) assumption for very short time-intervals only. In order to test this assumption we apply the concept of a local time- and frequency-variant scattering function, which we estimate from measurements of vehicle-to-vehicle wave propagation channels by means of a multi-window spectrogram. The obtained temporal sequence of local scattering functions (LSF) is used to calculate a collinearity measure. We define the stationarity time as the support of the region where the collinearity exceeds a certain threshold. The stationarity time is the maximum time duration over which the WSSUS assumption is valid. Measurements from an highway with vehicles driving in opposite directions show stationarity times as small as 23 ms whereas vehicles driving in the same direction show stationarity times of 1479 ms.

What makes a good MIMO channel model

Using different meaningful measures of quality, the paper investigates the accuracy of analytical MIMO channel models. Different metrics should be applied if the underlying MIMO channel supports predominantly beamforming, spatial multiplexing or diversity. The number of envisaged antennas plays an important role. By comparing the results of an extensive indoor measurement campaign at 5.2 GHz, we find the following main conclusions: (i) the recently developed Weichselberger model predicts capacity for any antenna number and represents diversity best of all three models, but still not satisfactorily; (ii) except for 2/spl times/2 MIMO systems, the Kronecker model fails to predict capacity, joint angular power spectrum, and diversity; (iii) the virtual channel representation should only be used for modeling the joint angular power spectrum for very large numbers of antennas. The answer to the question posed in the title: the appropriate model has to be chosen according to the considered application.