An efficient regularized semi-blind estimator

Abla Kammoun*, Karim Abed-Meraim, Sofiène Affes

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

This paper addresses the issue of the optimization of the regularization constant in semi-blind channel estimation techniques, in which the training sequence-based criterion is combined linearly with the blind subspace criterion. In such semi-blind estimation techniques, the optimization of the regularizing constant with respect to the channel estimation error is mandatory, otherwise, the expected improvement in performance could not be achieved. In this context, recent works proposed numerical methods for the setting of the regularization constant. However, these methods are often sub-optimum and involve high computational complexities. In this paper, we propose to optimize with respect to a regularizing matrix instead of a regularizing scalar. We prove that interestingly in this case, a closed-form expression for the optimum regularizing matrix exists, thereby avoiding iterative algorithms as for the conventional techniques. We also prove that the obtained scheme has slightly better performance in terms of mean square error and bit error rate while ensuring lower complexity.

Original languageEnglish (US)
Title of host publicationProceedings - 2009 IEEE International Conference on Communications, ICC 2009
DOIs
StatePublished - 2009
Externally publishedYes
Event2009 IEEE International Conference on Communications, ICC 2009 - Dresden, Germany
Duration: Jun 14 2009Jun 18 2009

Other

Other2009 IEEE International Conference on Communications, ICC 2009
CountryGermany
CityDresden
Period06/14/0906/18/09

Keywords

  • Asymptotic analysis
  • Regularization
  • Semi-blind equalization

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'An efficient regularized semi-blind estimator'. Together they form a unique fingerprint.

Cite this