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  • Research Article
  • Open Access

Application of the Evidence Procedure to the Estimation of Wireless Channels

EURASIP Journal on Advances in Signal Processing20072007:079821

  • Received: 5 November 2006
  • Accepted: 8 March 2007
  • Published:


We address the application of the Bayesian evidence procedure to the estimation of wireless channels. The proposed scheme is based on relevance vector machines (RVM) originally proposed by M. Tipping. RVMs allow to estimate channel parameters as well as to assess the number of multipath components constituting the channel within the Bayesian framework by locally maximizing the evidence integral. We show that, in the case of channel sounding using pulse-compression techniques, it is possible to cast the channel model as a general linear model, thus allowing RVM methods to be applied. We extend the original RVM algorithm to the multiple-observation/multiple-sensor scenario by proposing a new graphical model to represent multipath components. Through the analysis of the evidence procedure we develop a thresholding algorithm that is used in estimating the number of components. We also discuss the relationship of the evidence procedure to the standard minimum description length (MDL) criterion. We show that the maximum of the evidence corresponds to the minimum of the MDL criterion. The applicability of the proposed scheme is demonstrated with synthetic as well as real-world channel measurements, and a performance increase over the conventional MDL criterion applied to maximum-likelihood estimates of the channel parameters is observed.


  • General Linear Model
  • Graphical Model
  • Channel Model
  • Wireless Channel
  • Bayesian Framework

Authors’ Affiliations

Signal Processing and Speech Communication Laboratory, Graz University of Technology, Graz, 8010, Austria
Institute of Electronic Systems, Aalborg University, Fredrik Bajers Vej 7A, Aalborg, 9220, Denmark
Forschungszentrum Telekommunikation Wien (ftw.), Donau City Strasse 1, Wien, 1220, Austria


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© Dmitriy Shutin et al. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.