Title :
Unsupervised neural networks for multi-user detection in MC-CDMA systems
Author :
Carlier, Florent ; Nouvel, Fabienne
Author_Institution :
Inst. of Electron. & Telecommun. of Rennes, France
Abstract :
Performance and implementation complexity issues restrict standard multi-user detection methods in the forthcoming high transmission rate systems based on code division multiple access. We propose self-organizing neural networks to cope with this issue and suggest that an optimal multi-user detector can be implemented by using a Kohonen network.
Keywords :
3G mobile communication; cellular radio; code division multiple access; computational complexity; multiuser detection; radio receivers; self-organising feature maps; spread spectrum communication; telecommunication computing; unsupervised learning; Kohonen network; MC-CDMA; UMTS; cellular mobiles; code division multiple access; complexity; multi-user detection; multiuser detection; receiver; self-organizing neural networks; spread spectrum signals; unsupervised neural networks; Bandwidth; Detectors; Intelligent networks; Interference; Multiaccess communication; Multicarrier code division multiple access; Multiuser detection; Neural networks; Receivers; Spread spectrum communication;
Conference_Titel :
Personal Wireless Communications, 2002 IEEE International Conference on
Print_ISBN :
0-7803-7569-6
DOI :
10.1109/ICPWC.2002.1177288
