PROPAGATION CHANNEL MEASUREMENT AND MODELING
The performance of any transceiver, signal processing algorithm, channel code, etc., depends on the channel it is operating in. For wireless systems, this channel is the wireless propagation channel, whose properties differ significantly from the wired channel. A correct understanding and modeling of the propagation channel is thus a vital prerequisite for understanding the performance of wireless communications systems.
The difficulties in wireless channel modeling are due to the complex propagation processes that form the basis of a wireless channel, involving reflections, scattering, diffraction, and transmission through a large number of irregular objects. For all practical purposes, it is thus necessary to derive simplified descriptions. The degree of admissible simplification, in turn, depends on the system for which the channel is intended.
In the past 20 years, wireless systems have changed dramatically, from narrowband analogue systems, to wideband digital systems, to multi-antenna systems with even larger bandwidth. Last but not least, ultrawideband systems have emerged. As the systems have evolved, so have the channel models needed for the design and evaluation.
At the same time, the range of environments in which wireless systems are operating has greatly expanded. Twenty years ago, almost any wireless system had a base station (or broadcast transmitter) located far above the rooftops or other environmental structures. As time went on, microcellular systems (with a cellular BS antenna below rooftop height), indoor communications systems (e.g., WiFi), and peer-to-peer communications systems emerged. Those new environments require new measurements, new parameterization of channel models, and even the design of new models that are capable of describing effects unique to certain environments.
Measurement and Parameter Extraction Methods
As in all of science, thorough measurements are the conditio sine qua non for models and simulations, and the results of the measurements help to gain a deeper understanding of propagation channels. Channel characteristics that can be measured are impulse responses/transfer functions between transmit and receive antennas, and from those, we can derive secondary parameters.
It is important to keep in mind that a channel measurement is not simply taking a "channel sounder" and hitting the "start" button. Channel measurements require careful planning, proper selection of what is measured, where, and how often it is measured, and an understanding of what we ultimately want to extract from the measurements. For example, if we want to investigate the small-scale fading statistics of a channel, then we need a sufficient number of samples within a local region of stationarity (LRS) - and finding the size of an LRS is a difficult research topic. A related topic is that mathematical statistics is based on the concept of "ensembles", but identifying the physical quantities that constitute an ensemble is nontrivial. All these questions are, implicitly and explicitly, addressed in our measurement work.
After the measurements have been done, an extraction of parameters is required. Most measurements provide impulse responses or transfer functions between TX and RX. For multiple-antenna systems, we get a matrix of transfer functions (each entry corresponding to the transfer function between one TX antenna element and an RX antenna element). But in many cases, we are more interested in a parametric representation of the channel, describing where the multipath components (MPCs) are coming from, which delay they are suffering, and which amplitude they have. These MPC parameters can be extracted by means of so-called high-resolution algorithms, like SAGE (Space-Alternating Generalized Expectation-maximization) or CLEAN. We implement and apply such algorithms, and recently developed a method for tracking the evolution of MPCs in the delay/time domain for receivers that are moving over large distances.
Multipath components have a tendency to arrive in clusters, i.e., some MPCs have very similar delay and directions-of-arrival. The clustering of MPCs gives insights into the physics of the propagation (because MPCs belonging to one cluster usually have undergone similar propagation processes), and also allow a more compact modeling. While early work on clustering used "eyeballing" of plots of MPCs in the delay/angle domain, we are now employing automated clustering based on the concept of "multipath distance".
Fundamental Channel Models for MIMO Systems
With the emergence of multiple-antenna systems in the 1990s, new channel models became necessary. The first theoretical treatments of MIMO systems used the "channel impulse response matrix" h, where each element hij contains the impulse response from the j-th transmit to the i-th receive antenna element. The main drawback of this characterization is that it mixes up the contributions of the antenna characteristics, and is valid only for one particular antenna arrangement. We thus proposed the double-directional impulse response h(tau,phiTX,phiRX), which describes how radiation launched into the direction phiTX is delayed, and from directions phiRX it arrives at the receiver. This double-directional impulse response can be combined with any antenna configuration, and has been adopted by pretty much every standard MIMO channel model. Our current research revolves around large-scale variations of the double-directional impulse response, and whether it can be expanded into a finite sum of discrete contributions, or a "diffuse background" should be included.
Another direction of research revolves around "geometry-based stochastic channel models" (GSCMs). The basic principle is to statistically model the distribution of scatterers in space (by prescribing a probability density function of their location); impulse responses (including double-directional impulse responses) can then be found by a simplified ray tracing procedure that assumes that only single-scattering or double-scattering processes can occur. The origins of GSCMs go back to the 1970s, when a rings of scatterers around the MS was used to compute the effectiveness of diversity antennas at the BS. We were the first to generalize the model to the MIMO case, showing that many of the simplifications of the "diversity-antenna" scenario do not hold for MIMO, and proposing new modeling methods that solved this problem. Our current work focuses on adopting this model to the specific challenges of car-to-car propagation channels.
Car-to-car and Railway Communications
Car-to-car communications have many envisioned applications in traffic safety and congestion avoidance. For example, sensor-equipped cars that communicate via wireless links and thus build up adhoc networks can be used to reduce traffic accidents and facilitate traffic flow. Car-to-car propagation channels are clearly non-stationary, as the environment in which TX and RX are located changes dynamically, often on a timescale of seconds. The figure below shows a typical result - it is clear how the absolute delay of the multipath components, as well as the relative delay between them, changes over time.
A channel model for car-to-car communications is best based on the GSCM approach, which can naturally deal with non-stationary environments. Below, we show one example for a model that is suitable for a highway environment (urban environments exhibit stronger scatterers by the roadside).Furthermore, the channel model is suitable for MIMO systems, which can be anticipated to be used in next-generation car-to-car communications.
High-speed railways (HSR) are low-cost and environmentally friendly means of mass transportation over large distance, which has been widely introduced within the past few decades. Meanwhile, a reliable communication system with high capacity and security is a challenging task for HSR. Due to the flatness requirement of rails to ensure the safety of high-speed train, many new rail structures are introduced, such as viaducts, cuttings, tunnels, etc., whose geometrical layouts are entirely different from standard rural/suburban environments. An accurate characterization of the propagation channels in these scenarios is thus an important, though challenging, task.
Below, we show a measurement-based path loss model in viaduct scenario of HSR. We note that the high viaduct leads to clear LOS component and small propagation attenuation. Meanwhile, the railway-specific directional base station antenna leads to a break point in the path loss model. Furthermore, the special structure of viaduct also affects the distance-dependent variation of the Ricean K-factor.
Ultra-wideband Channel Measurement and Modeling
Modeling of Wireless Propagation Channels for Joint Multi-link Communications and Ray Tracing
Modeling of wireless propagation channels for joint multi-link communications
This project objective is to give a generic channel model for joint-multi-link that can be parameterized by measurements and/or ray tracing results. We will generalize the existing stochastic and geometric approaches to handle joint multi-link in different environments, such as metropolitan, campus, and indoor office, typical urban and suburban environment.
From the statistic perspective, we need to model the random process of large scale parameters, including shadowing, delay spread, angular spread, and their correlation between different links. From the geometric perspective, we can use ray tracing simulation to generate a cite-specific deterministic multi-link channel model for a certain kind of environment.
Ray Tracing Simulation
We are running commercialized ray tracing software Wireless InSite in various environments and obtain channel characteristics for both BS to MS link and MS to MS links. Currently, we are exploring statistics of the impulse response as well as narrowband characteristics, such as shadowing process.
The simulation process is divided into two parts. In the first one, BS is the transmitter and all MSs are receivers, and ray tracer simulates all possible BS to MS links. In the second set, one of MS routes is picked to be a transmitter route while all the other MS routes remain to be receivers.
Ray-tracer will generate simulation result files that contain information about the impulse response, multipath component characteristics. This provides database for studying correlation statistics in peer-to-peer communication channel.
Development of Channel Sounder
We currently posses an Ultrawideband MIMO channel Sounder (Figure 1) designed and assembled in-house for the characterization of a UWB radio propagation channel. While this device as performed remarkable well, we are currently working on several improvement and generalization of its functionalities.
Figure 1. Real-time UWB MIMO Channel Sounding Setup
Figure 2. Real-time UWB MIMO Channel Sounding assembled in an anechoic chamber
A. F. Molisch, “Propagation and channel modeling principles”, in G. de la Roche, A. A. Glazunov, and B. Allen, “LTE Advanced and Beyond Wireless Networks: Channel Modeling and Propagation”, Wiley (2012).
L. Bernado, N. Czink, T. Zemen, A. Paier, F. Tufvesson, C. Mecklenbraueker, and A. F. Molisch, “Vehicular channels”, in G. de la Roche, A. A. Glazunov, and B. Allen, “LTE Advanced and Beyond Wireless Networks: Channel Modeling and Propagation”, Wiley (2012)
L. Greenstein, M. Shafi, and A. F. Molisch, “Propagation effects in cognitive radio”, in E. Biglieri et al. (eds)., “Cognitive Radio Principles”, Cambridge University Press (2012).
R. He, Z. Zhong, B. Ai, G. Wang, J. Ding, and A. F. Molisch, “Measurements and Analysis of Channel Fading Behavior in the Railway Viaduct Scenario”, IEEE Trans. Wireless Comm., accepted.
N. Michelusi, U. Mitra, A. F. Molisch, and M. Zorzi, “UWB Sparse/Diffuse Channels, Part II: Estimator Analysis and Practical Channels”, IEEE Trans. Signal Process., in press.
N. Michelusi, U. Mitra, A. F. Molisch, and M. Zorzi, “UWB Sparse/Diffuse Channels, Part I: Channel Models and Bayesian Estimators”, IEEE Trans. Signal Process., in press
K. Haneda, A. Richter, and A. F. Molisch, “Modeling the Frequency Dependence of Ultrawideband Spatio-Temporal Indoor Radio Channels”, IEEE Trans. Antennas Propagation, 60, 2940 – 2950 (2012).
C. Mecklenbraueker, A. F. Molisch, J. Karedal, F. Tufvesson, A. Paier, L. Bernardo, T. Zemen, O. Klemp, N. Czink, “Vehicular Channel Characterization and its Implications for Wireless System Design and Performance”, Proc. IEEE, 99, 1189-1212 (2011).
J. Karedal, F. Tufvesson, A. Paier, N. Czink, and A. F. Molisch, “Four Pathloss Models for Vehicle-to-Vehicle Communications”, IEEE Trans. Vehicular Technology, 60, 323-327 (2011).
A. Alayon Glazunov, M. Gustafsson, and A.F. Molisch, “On the physical limitations of the interaction of a spherical aperture and a random field”, IEEE Trans. Antennas and Propagation, 59, 119 – 128 (2011).
A. Alayon Glazunov, M. Gustafsson, A. F. Molisch, and F. Tufvesson “Physical Modeling of MIMO Antennas and Channels by Means of the Spherical Vector Wave Expansion”, IET Microwaves Antennas and Propagation, 6, 778-791 (2010).
F. Harryson, J. Medbo, A. F. Molisch, A. Johansson, and F. Tufvesson, “Efficient Experimental Evaluation of a MIMO Handset with User Influence”, IEEE Trans. Wireless Comm., 9, 853-863 (2010).
T. Santos, F. Tufvesson, and A. F. Molisch, “Modeling the UWB Outdoor Channel – Model Specification and Validation”, IEEE Trans. Wireless Comm., 9, 1987-1997 (2010).
T. Santos, P. Almers, J. Karedal, F. Tufvesson, and A. F. Molisch, “Modeling the UWB Outdoor Channel - Measurements and Parameter Extraction Method”, IEEE Trans. Wireless Comm., 9, 282-290 (2010).
S. Wyne, A. Singh, T. Santos, F. Tufvesson, and A. F. Molisch, “Characterization of a Time-Variant Wireless Propagation Channel for Outdoor Short-Range Sensor Networks”, IET Communications 4, 253-264 (2010).
J. Karedal, P. Almers, A. J Johansson, F. Tufvesson, and A. F. Molisch, “A MIMO Channel Model for Wireless Personal Area Networks”, IEEE Trans. Wireless Comm., 8, 245-255 (2010).
V. M. Kolmonen, P. Almers, J. Salmi, J. Koivunen, K. Haneda, A. Richter, F. Tufvesson, A. F. Molisch, and P. Vainikainen, A Dynamic Dual-Link Wideband MIMO Channel Sounder for 5.3 GHz”, IEEE Trans. Instrum. Meas., 59, 873-883 (2010).
A. F. Molisch, F. Tufvesson, J. Karedal, and C. Mecklenbrauker, “Propagation Aspects of Vehicle-to-Vehicle Communications”, IEEE Wireless Communications Magazine, 16, Issue 6, 12 – 22 (2009).
S. Wyne, A. Singh, F. Tufvesson, and A. F. Molisch, “A Statistical Model for Indoor Office Wireless Sensor Channels”, IEEE Trans. Wireless Comm., 8, 4154-4164 (2009).
J. Karedal, F. Tufvesson, N. Czink, A. Paier, C. Dumard, T. Zemen, C. F. Mecklenbraeuker, and A. F. Molisch, “A Geometry-Based Stochastic MIMO Model for Vehicle-to-Vehicle Communications”, IEEE Trans. Wireless Comm., 8, 3646-3657 (2009).
A. F. Molisch, M. Shafi, and L. J. Greenstein, “Propagation Issues for Cognitive Radio”, Proc. IEEE, special issue on cognitive radio, 97, 787-804 (2009). Donald Fink Award of IEEE.
A. F. Molisch, “Ultrawideband propagation channels”, Proc. IEEE, special issue on UWB, 97, 353-371, (2009).
A. Alayon Glazunov, M. Gustafsson, A. F. Molisch, F. Tufvesson, and G. Kristensson “Spherical Vector Wave Expansion of Gaussian Electromagnetic Fields for Antenna-Channel Interaction Analysis”, IEEE Trans. Antennas and Propagation, 57, 2055-2067 (2009).
A. Alayon Glazunov, A. F. Molisch, and F. Tufvesson, “On mean effective gain of antennas”, Proceeding IET Microwaves, Antennas & Propagation, 3, 214-227, 2009.
Zheda Li, Rui Wang, and A.F. Molisch, ”Shadowing in urban environments with micro- cellular or peer-to-peer links, ”in European Conference on Antennas and Propagation (EUCAP), 2012 IEEE 6rd, pp. 44-48 , IEEE, 2012
A. F. Molisch, “MIMO-UWB propagation channels”, in EuCAP
L. Bernado, T. Zemen, F. Tufvesson, A. F. Molisch, and C. Mecklenbraueker, “The (in-)validity of the WSSUS Assumption in Vehicular Radio Channels”, IEEE PIMRC
N. Michelusi, U. Mitra, A. F. Molisch, and M. Zorzi, “Hybrid sparse/diffuse channels: A new model and estimators for wideband channels”, 49th Annual Allerton Conf.
T. Abbas, J. Karedal, F. Tufvesson, A. Paier, Laura Bernado, and A. F. Molisch, “Directional Analysis of Vehicle-to-Vehicle Propagation Channels”, IEEE VTC 2011 spring
L. Bernado, A. Roma, T. Zemen, N. Czink, J. Karedal, A. Paier, A. Thiel, F. Tufvesson, A. F. Molisch, C. F. Mecklenbrauker, “In-Tunnel Vehicular Radio Channel Characterization”, IEEE VTC 2011 spring
C. Gustafson, F. Tufvesson, S. Wyne, K. Haneda and A. F. Molisch, “Directional Analysis of Measured 60 GHz Indoor Radio Channels using SAGE”, IEEE VTC 2011 spring
L. Bernardo, T. Zemen, J. Karedal, A. Paier, A. Thiel, N. Czink, F. Tufvesson, A.F. Molisch, and C. Mecklenbraeuker, “Multi-Dimensional K-Factor Analysis for V2V Radio Channels in Open Sub-urban Street Crossings”, PIMRC 2010
, 1-5 (2010).
J. Karedal, F. Tufvesson, T. Abbas, O. Klemp, A. Paier, L. Bernado, and A. F. Molisch, “Radio Channel Measurements at Street Intersections for Vehicle-to-Vehicle Safety Applications”, IEEE VTC 2010 spring
, 1-5 (2010).
A. F. Molisch, F. Tufvesson, J. Karedal, and C. Mecklenbrauker, "Propagation aspects of vehicle-to-vehicle communications - an overview", IEEE Radio and Wireless Symposium
, p. 1-4, 2009.
A. Paier, T. Zemen, J. Kåredal, N. Czink, C. Dumard, F. Tufvesson, C. F. Mecklenbräuker, A.F. Molisch, “Spatial Diversity and Spatial Correlation Evaluation of Measured Vehicle-to-Vehicle Radio Channels at 5.2 GHz”, 2009 DSP Workshop
, 326-330 (2009).
J. Karedal, F. Tufvesson, N. Czink, A. Paier, C. Dumard, T. Zemen, C. F. Mecklenbraeuker, and A. F. Molisch, “Measurement-Based Modeling of Vehicle-to-Vehicle MIMO Channels”, IEEE ICC 2009