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Publications in Scientific Journals:

T. Blazek, C. Mecklenbräuker:
"Measurement-Based Burst-Error Performance Modeling for Cooperative Intelligent Transport Systems";
IEEE Transactions on Intelligent Transportation Systems, 20 (2019), 1; 162 - 171.



English abstract:
Safety applications for cooperative intelligent transport
systems are limited in their performance by the latency of the
communication more than by the achieved throughput. However,
there exist few models at packet level that are able to capture
the burstiness of the communication. We therefore introduce a
packet error model that considers burst lengths through secondorder
statistics and mean packet errors. The foundation of our
approach is the Gilbert-Elliot model, which is able to model not
only the packet error rate, but also the burst durations of the
packet errors, which we interpret in a nonstationary fashion.
Based on this, we formulate maximum likelihood expressions for
the time variant model fits, and then proceed to fit the parameters
to extensive recorded measurements. We consider the fading statistics
of the measured channel and the signal-to-noise ratio and
present how they influence the channel burstiness. Our analysis
demonstrates that the communication shows strong bursts at
packet level, proving the demand for such models. The approach
we demonstrate here remains of low computational complexity,
allowing future employment in large-scale simulations.

German abstract:
Safety applications for cooperative intelligent transport
systems are limited in their performance by the latency of the
communication more than by the achieved throughput. However,
there exist few models at packet level that are able to capture
the burstiness of the communication. We therefore introduce a
packet error model that considers burst lengths through secondorder
statistics and mean packet errors. The foundation of our
approach is the Gilbert-Elliot model, which is able to model not
only the packet error rate, but also the burst durations of the
packet errors, which we interpret in a nonstationary fashion.
Based on this, we formulate maximum likelihood expressions for
the time variant model fits, and then proceed to fit the parameters
to extensive recorded measurements. We consider the fading statistics
of the measured channel and the signal-to-noise ratio and
present how they influence the channel burstiness. Our analysis
demonstrates that the communication shows strong bursts at
packet level, proving the demand for such models. The approach
we demonstrate here remains of low computational complexity,
allowing future employment in large-scale simulations.

Keywords:
Intelligent transportation systems, measurement, performance analysis


"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)
http://dx.doi.org/10.1109/TITS.2018.2803266


Created from the Publication Database of the Vienna University of Technology.