Partial Parallel Interference Cancellation Multiuser Detection using Recurrent Neural Network Based on Hebb Learning Rule

Author

Li, Yanping ; Zhang, Yongbo ; Wang, Huakui

Author_Institution

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

Volume

1

fYear

0

fDate

0-0 0

Firstpage

2989

Lastpage

2992

Abstract

In CDMA communication systems, in order to decrease the influence on reception performance resulted from incorrect decision of the interference users\´ information bits in parallel interference cancellation (PIC) process, a recurrent neural network based on Hebb learning rule is designed and applied to adjusting interference cancellation factors (ICF) in partial parallel interference cancellation (PPIC) multiuser detection. Simulation results show that the proposed Hebb-PPIC detection has strong anti-MAI ability and its performance of bit error rate (BER) is improved on the basis of conventional PIC in both conditions of ideal power control and "near-far" scenario

Keywords

Hebbian learning; code division multiple access; error statistics; interference suppression; multiuser detection; recurrent neural nets; telecommunication computing; CDMA communication systems; Hebb learning rule; antiMAI ability; bit error rate; interference cancellation factors; multiuser detection; partial parallel interference cancellation; power control; recurrent neural network; AWGN; Bit error rate; Filtering; Interference cancellation; Mathematical model; Multiaccess communication; Multiple access interference; Multiuser detection; Power control; Recurrent neural networks; Hebb learning rule; multiuser detection; partial parallel interference cancellation (PPIC); recurrent neural network;

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.2006.1712914

Filename

1712914