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Cluster-Based Scatterer Identification and Characterization in Vehicular Channels

Bernadó, Laura; Roma, Anna; Czink, Nicolai; Kåredal, Johan LU ; Paier, Alexander and Zemen, Thomas (2011) European Wireless Conference
Abstract
In this paper, we present a new approach for the identification of scattering objects in the delay and Doppler domains. Until now, the identification was done visually based on the power delay profile and video material recorded in the measurement campaigns. We propose to use automatic methods based on the local scattering function (LSF), which brings the

Doppler domain into play.

The LSF is a multitaper estimate of the two-dimensional (2D) power spectral density in delay and Doppler. Each peak of the LSF is composed of several multipath components (MPCs) coming from the same scattering object.

Our approach consists of two steps: detection of the relevant peaks, and assignment of MPCs to the scattering objects... (More)
In this paper, we present a new approach for the identification of scattering objects in the delay and Doppler domains. Until now, the identification was done visually based on the power delay profile and video material recorded in the measurement campaigns. We propose to use automatic methods based on the local scattering function (LSF), which brings the

Doppler domain into play.

The LSF is a multitaper estimate of the two-dimensional (2D) power spectral density in delay and Doppler. Each peak of the LSF is composed of several multipath components (MPCs) coming from the same scattering object.

Our approach consists of two steps: detection of the relevant peaks, and assignment of MPCs to the scattering objects using a clustering algorithm. We apply the method to a set of vehicular radio channel measurements and extract the time-varying cluster parameters.

The clusters have ellipsoidal shape with their longer axis in the Doppler domain. The first detected cluster presents different properties than the rest of the clusters, being larger, constant in time, and more static in the delay-Doppler plane. By properly identifying only the relevant scattering objects, vehicular channel models, such as the geometry-based stochastic channel model, can be simplified significantly. (Less)
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author
organization
publishing date
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Contribution to conference
publication status
submitted
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pages
5 pages
conference name
European Wireless Conference
language
English
LU publication?
yes
id
76a64ad4-b2be-403e-9e3c-11d15b9d5a6d (old id 1782193)
date added to LUP
2011-02-04 12:24:01
date last changed
2016-04-16 11:15:44
@misc{76a64ad4-b2be-403e-9e3c-11d15b9d5a6d,
  abstract     = {In this paper, we present a new approach for the identification of scattering objects in the delay and Doppler domains. Until now, the identification was done visually based on the power delay profile and video material recorded in the measurement campaigns. We propose to use automatic methods based on the local scattering function (LSF), which brings the<br/><br>
Doppler domain into play.<br/><br>
The LSF is a multitaper estimate of the two-dimensional (2D) power spectral density in delay and Doppler. Each peak of the LSF is composed of several multipath components (MPCs) coming from the same scattering object.<br/><br>
Our approach consists of two steps: detection of the relevant peaks, and assignment of MPCs to the scattering objects using a clustering algorithm. We apply the method to a set of vehicular radio channel measurements and extract the time-varying cluster parameters.<br/><br>
The clusters have ellipsoidal shape with their longer axis in the Doppler domain. The first detected cluster presents different properties than the rest of the clusters, being larger, constant in time, and more static in the delay-Doppler plane. By properly identifying only the relevant scattering objects, vehicular channel models, such as the geometry-based stochastic channel model, can be simplified significantly.},
  author       = {Bernadó, Laura and Roma, Anna and Czink, Nicolai and Kåredal, Johan and Paier, Alexander and Zemen, Thomas},
  language     = {eng},
  pages        = {5},
  title        = {Cluster-Based Scatterer Identification and Characterization in Vehicular Channels},
  year         = {2011},
}