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A Practical Subspace Blind Identification Algorithm with Reduced Computational Complexity
Nari TANABE Toshihiro FURUKAWA Kohichi SAKANIWA Shigeo TSUJII
IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences
Publication Date: 2004/12/01
Print ISSN: 0916-8508
Type of Manuscript: PAPER
Category: Digital Signal Processing
principal component analysis, autocorrelation, subspace method, eigenvalue and singular-value decomposition, computational complexity, channel order, noise variance,
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We propose a practical blind channel identification algorithm based on the principal component analysis. The algorithm estimates (1) the channel order, (2) the noise variance, and then identifies (3) the channel impulse response, from the autocorrelation of the channel output signal without using the eigenvalue and singular-value decomposition. The special features of the proposed algorithm are (1) practical method to find the channel order and (2) reduction of computational complexity. Numerical examples show the effectiveness of the proposed algorithm.