DocumentCode
2206358
Title
A neural network for smallest eigenvalue with application to blind equalization
Author
Dong, Guojie ; Liu, Ruey-wen
Author_Institution
Dept. of Electr. Eng., Notre Dame Univ., IN, USA
Volume
2
fYear
1995
fDate
13-16 Aug 1995
Firstpage
811
Abstract
In this paper, a neural network is presented which can extract the smallest eigenvalue and its associated eigenvector of the autocorrelation matrix of a incoming vector of stochastic process. It is also shown that the domain of convergence is the unit sphere, substantiated by computer simulation. We also demonstrate by simulation that this neural network is capable to do blind equalization which is crucial for wireless communication
Keywords
convergence; correlation theory; eigenvalues and eigenfunctions; equalisers; neural nets; stochastic processes; autocorrelation matrix; blind equalization; computer simulation; convergence; eigenvalue; eigenvector; neural network; stochastic process; vector; wireless communication; Application software; Autocorrelation; Blind equalizers; Computational modeling; Computer simulation; Convergence; Eigenvalues and eigenfunctions; Neural networks; Stochastic processes; Wireless communication;
fLanguage
English
Publisher
ieee
Conference_Titel
Circuits and Systems, 1995., Proceedings., Proceedings of the 38th Midwest Symposium on
Conference_Location
Rio de Janeiro
Print_ISBN
0-7803-2972-4
Type
conf
DOI
10.1109/MWSCAS.1995.510212
Filename
510212
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