Complexity reduction for vehicular channel estimation using the filter divergence measure

Laura Bernadó, Thomas Zemen, Alexander Paier, Johan Karedal

Research output: Chapter in Book or Conference ProceedingsConference Proceedings with Oral Presentationpeer-review


A key component in vehicular communications systems is the channel estimation filter that suppresses the additive noise in the channel estimates from pilot symbols. A filter which
offers the best performance in terms of mean square error (MSE) is the well known Wiener filter. A drawback of using filters based on second order statistics is that they have to be
recalculated when the statistical properties of the channel have changed. In vehicular communications the observed channels
do not follow the wide-sense stationary (WSS) uncorrelated-scattering (US) properties, and therefore their power spectral density varies over time. A non-stationary process can be divided in time into consecutive stationarity regions where the WSS and US properties are assumed to hold, allowing to calculate
the coefficients of a Wiener filter. In this paper we analyze the increase of the MSE observed when using a mismatched Wiener filter. The mismatch results from using the filter coefficients calculated for a past stationarity region. We relate this concept of performance degradation to spectral distance metrics. We use
the spectral divergence between scattering functions at different time instances. Furthermore, we introduce a new metric, the
filter divergence, which takes noise into account. We show that, by accepting an increase of MSE, the same filter coefficients can
be used for several time regions, which allows computational complexity reduction in a real system.
Original languageEnglish
Title of host publication2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers
Number of pages5
ISBN (Electronic)978-1-4244-9721-8
Publication statusPublished - 2010

Research Field

  • Enabling Digital Technologies


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