Bilinear Neural Network Tracking Subspace for Blind Multiuser Detection

Author

Zhang, Yongbo ; Li, Yanping ; Wang, Huakui

Author_Institution

Dept. of Inf. Eng., Taiyuan Univ. of Technol.

Volume

1

fYear

0

fDate

0-0 0

Firstpage

4927

Lastpage

4930

Abstract

In order to lower the level of multi-access interference (MAI) and improve the reception performance in CDMA communication systems, a new blind multiuser detection algorithm is proposed. Different from some other blind detection approaches, the proposed algorithm tracks the eigenvalues and eigenvectors of signal subspace by bilinear neural network based on a new principal component analysis (PCA) method. Simulation results show that the proposed algorithm improves the tracking capability and bit error rate (BER) performance of blind detector compared with the MMSE and PASTd algorithm. In consideration of its comparatively low computational complexity, the proposed detection has some value in practice

Keywords

code division multiple access; eigenvalues and eigenfunctions; error statistics; interference (signal); multiuser detection; neural nets; principal component analysis; CDMA communication systems; bilinear neural network tracking subspace; bit error rate; blind multiuser detection; eigenvalues; eigenvectors; multiaccess interference; principal component analysis; signal subspace; Bit error rate; Computational complexity; Computational modeling; Detectors; Eigenvalues and eigenfunctions; Multiaccess communication; Multiple access interference; Multiuser detection; Neural networks; Principal component analysis; bilinear neural network; blind multiuser detection; principal component analysis (PCA); subspace eigenvalue;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Conference_Location

Dalian

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1713322

Filename

1713322