Measurement-based modeling of vehicle-to-vehicle MIMO channels

Authors:
Johan Karedal;Fredrik Tufvesson;Nicolai Czink;Alexander Paier;Charlotte Dumard;Thomas Zemen;Christoph F. Mecklenbräuker;Andreas F. Molisch
Affiliations:
Dept. of Electrical and Information Technology, Lund University, Lund, Sweden;Dept. of Electrical and Information Technology, Lund University, Lund, Sweden;Forschungszentrum Telekommunikation Wien, Vienna, Austria and Stanford University, Stanford, CA;Inst. für Nachrichtentechnik und Hochfrequenztechnik, Technische Universität Wien, Vienna, Austria;Forschungszentrum Telekommunikation Wien, Vienna, Austria;Forschungszentrum Telekommunikation Wien, Vienna, Austria;Forschungszentrum Telekommunikation Wien, Vienna, Austria and Inst. für Nachrichtentechnik und Hochfrequenztechnik, Technische Universität Wien, Vienna, Austria;Dept. of Electrical Engineering, University of Southern California, Los Angeles, CA
Venue:
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Year:
2009

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Abstract

Vehicle-to-vehicle (VTV) communications are of interest for applications within traffic safety and congestion avoidance, but the development of suitable communications systems requires accurate models for VTV propagation channels. This paper presents a new wideband MIMO (multiple-input-multiple-output) channel model for VTV channels based on extensive MIMO channel measurements performed at 5.2 GHz in rural environments in Lund, Sweden. The measured channel characteristics, in particular the non-stationarity 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 find it suitable to distinguish between diffuse and discrete scattering contributions. The time-variant contributions from discrete scatterers are tracked over time and delay using a high resolution algorithm, and our observations motivate their power being modeled as a combination of a deterministic part and a stochastic part. The paper gives a full model parameterization and the model is verified by comparison of MIMO antenna correlations derived from the channel model to those obtained directly from measurements.