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Blind Identification of FIR Channels in the Presence of Unknown Noise

EURASIP Journal on Advances in Signal Processing volume 2007, Article number: 012172 (2007) Cite this article

Abstract

Blind channel identification techniques based on second-order statistics (SOS) of the received data have been a topic of active research in recent years. Among the most popular is the subspace method (SS) proposed by Moulines et al. (1995). It has good performance when the channel output is corrupted by white noise. However, when the channel noise is correlated and unknown as is often encountered in practice, the performance of the SS method degrades severely. In this paper, we address the problem of estimating FIR channels in the presence of arbitrarily correlated noise whose covariance matrix is unknown. We propose several algorithms according to the different available system resources: (1) when only one receiving antenna is available, by upsampling the output, we develop the maximum a posteriori (MAP) algorithm for which a simple criterion is obtained and an efficient implementation algorithm is developed; (2) when two receiving antennae are available, by upsampling both the outputs and utilizing canonical correlation decomposition (CCD) to obtain the subspaces, we present two algorithms (CCD-SS and CCD-ML) to blindly estimate the channels. Our algorithms perform well in unknown noise environment and outperform existing methods proposed for similar scenarios.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, McMaster University, Hamilton, Ontario, L8S 4K1, Canada

    Xiaojuan He & Kon Max Wong

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  1. Xiaojuan He
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Correspondence to Xiaojuan He.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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He, X., Wong, K.M. Blind Identification of FIR Channels in the Presence of Unknown Noise. EURASIP J. Adv. Signal Process. 2007, 012172 (2007). https://doi.org/10.1155/2007/12172

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