Multi-user detection in MC-CDMA systems and unsupervised neural network: why?

Author

Carlier, F. ; Nouvel, Fabienne

Author_Institution

Inst. of Electron. & Telecommun. of Rennes, France

Volume

3

fYear

2003

fDate

14-17 Dec. 2003

Firstpage

1336

Abstract

This paper is about a MC-CDMA detector based on unsupervised neural network. Systems based on MC-CDMA techniques generally require quite a complex implementation to achieve high data rates. The solution we will consider here, offers less complexity as well as realtime execution. The idea is to allow the implementation of a simulated system in a realtime system within the field of standard multi-user detection methods.

Keywords

3G mobile communication; 4G mobile communication; cellular radio; code division multiple access; multiuser detection; self-organising feature maps; spread spectrum communication; telecommunication computing; unsupervised learning; Kohonen neural network; MC-CDMA systems; hexagonal lattice structures; learning rate functions; less complexity; mobile cellular networks; multiuser detection; realtime execution; spread spectrum communications; unsupervised neural network; Additive white noise; Detectors; Integrated circuit noise; Intelligent networks; Interference; Multiaccess communication; Multicarrier code division multiple access; Multiuser detection; Neural networks; Spread spectrum communication;

fLanguage

English

Publisher

ieee

Conference_Titel

Electronics, Circuits and Systems, 2003. ICECS 2003. Proceedings of the 2003 10th IEEE International Conference on

Print_ISBN

0-7803-8163-7

Type

conf

DOI

10.1109/ICECS.2003.1301762

Filename

1301762