Blind Identification of FIR Channels in the Presence of Unknown Noise
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Research Article Blind Identification of FIR Channels in the Presence of Unknown Noise Xiaojuan He and Kon Max Wong Department of Electrical and Computer Engineering, McMaster University, Hamilton, Ontario, Canada L8S 4K1 Received 23 December 2005; Revised 20 July 2006; Accepted 29 October 2006 Recommended by Markus Rupp 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. Copyright © 2007 X. He and K. M. Wong. 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.
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INTRODUCTION
Channel distortion remains one of the hurdles in highfidelity data communications because the performance of a digital communication system is invariably affected by the characteristics of the channel over which the signals are transmitted as well as by additive noise. The effects of the channel often manifest themselves as distortions to the transmitted signals in the form of intersymbol interference (ISI), cross-talks, fading, and so forth [2]. Mitigation of such effects is often carried out by filtering, channel equalization, and appropriate signal designs for which a proper knowledge of the channel characteristics is required. Thus, channel estimation is a very important process in digital communications. Traditionally, channel estimation is carried out by observing the received pilot signals sent over the channel and various algorithms for identifying the channel have been developed based on the transmission of pilot signals [3–5]. However, the insertion of pilot signals often means a decrease of bandwidth efficiency, and the resulting limitation of effective data throughput [6] may be a substantial penalty in performance. Thus, blind identification of the channel could be he
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