A practical subspace blind identification algorithm with reduced computational complexity

被引：0
|
作者：
Tanabe, N [1 ]
Furukawa, T
Sakaniwa, K
Tsujii, S
机构：
[1] Tokyo Inst Technol, Dept Commun & Integrated Syst, Tokyo 1528552, Japan
[2] Tokyo Univ Sci, Dept Management Sci, Tokyo 1620825, Japan
[3] Inst Informat Secur, Grad Sch Informat Secur, Yokohama, Kanagawa 2210835, Japan
来源：
IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES | 2004年 / E87A卷 / 12期
关键词：
principal component analysis; autocorrelation; subspace method; eigenvalue and singular-value decomposition; computational complexity; channel order; noise variance;
D O I：
暂无
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：3360 / 3371
页数：12
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