NDLI: Bi-iterative least squares algorithms for blind channel identification and equalization with second-order statistics

Content Provider	Springer Nature Link
Author	Ouyang, Shan
Copyright Year	2009
Abstract	We present an adaptive algorithm for blind identification and equalization of single-input multiple-output (SIMO) FIR channels with second-order statistics. We first reformulate the blind channel identification problem into a low-rank matrix approximation solution based on the QR decomposition of the received data matrix. Then, a fast recursive algorithm is developed based on the bi-iterative least squares (Bi-LS) subspace tracking method. The new algorithm requires only a computational complexity of O(md $^{2}$) at each iteration, or even as low as O(md) if only equalization is necessary, where m is the dimension of the received data vector (or the row rank of channel matrix) and d is the dimension of the signal subspace (or the column rank of channel matrix). To overcome the shortcoming of the back substitution, an inverse QR iteration algorithm for subspace tracking and channel equalization is also developed. The inverse QR iteration algorithm is well suited for the parallel implementation in the systolic array. Simulation results are presented to illustrate the effectiveness of the proposed algorithms for the channel identification and equalization.
Starting Page	1905
Ending Page	1914
Page Count	10
File Format	PDF
ISSN	10092757
Journal	Science in China Series : Information Sciences
Volume Number	52
Issue Number	10
e-ISSN	18622836
Language	English
Publisher	SP Science in China Press
Publisher Date	2009-10-06
Publisher Place	Heidelberg
Access Restriction	Subscribed
Subject Keyword	intersymbol interference interference blind identification and equalization subspace tracking low-rank approximation second-order statistics QR-decomposition inverse QR iteration bi-iteration SIMO Information Systems and Communication Service
Content Type	Text
Resource Type	Article
Subject	Computer Science

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