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T. Blazek, E. Zöchmann, C. Mecklenbräuker:
"Approximating Clustered Millimeter Wave Vehicular Channels by Sparse Subband Fitting";
Talk: 2018 IEEE 29th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), Bologna, Italy; 09-09-2018 - 09-12-2018; in: "2018 IEEE 29th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)", IEEE (ed.); IEEE, (2018), ISBN: 978-1-5386-6009-6; 5 pages.

Understanding millimeter wave (mmWave) vehicular
channels is crucial for the application of mmWave
technologies in vehicle-to-anything settings. However, as of
yet, few attempts of low-complexity approximation of such
channels exist. Prior results have shown that such channels
are often composed of clustered multipath components, and
this work builds on those results. We present an approach
to project a measured mmWave channel into subbands.
Sufficiently narrow subbands do not resolve the cluster
structures and are efficiently approximated as sparse channels.
We thereby render sparse tapped delay line model
fits possible. In this contribution, we optimize sparse fits
in subbands, and then combine all fits to approximate
the full band. We evaluate this approach using vehicular
mmWave channel measurements, and demonstrate that
subband fitting results in efficient leveraging of sparse
structures of mmWave channel data.

Understanding millimeter wave (mmWave) vehicular
channels is crucial for the application of mmWave
technologies in vehicle-to-anything settings. However, as of
yet, few attempts of low-complexity approximation of such
channels exist. Prior results have shown that such channels
are often composed of clustered multipath components, and
this work builds on those results. We present an approach
to project a measured mmWave channel into subbands.
Sufficiently narrow subbands do not resolve the cluster
structures and are efficiently approximated as sparse channels.
We thereby render sparse tapped delay line model
fits possible. In this contribution, we optimize sparse fits
in subbands, and then combine all fits to approximate
the full band. We evaluate this approach using vehicular
mmWave channel measurements, and demonstrate that
subband fitting results in efficient leveraging of sparse
structures of mmWave channel data.

mmWave, Vehicular Channel Models, cLASSO, Cluster I

https://publik.tuwien.ac.at/files/publik_271472.pdf