DocumentCode
3146007
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
fYear
2002
fDate
15-17 Dec. 2002
Firstpage
255
Lastpage
259
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Personal Wireless Communications, 2002 IEEE International Conference on
Print_ISBN
0-7803-7569-6
Type
conf
DOI
10.1109/ICPWC.2002.1177288
Filename
1177288
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