| Paper Details: | Downloads: 316 |
| Serial Number: | P1120634005
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| Title: | A Comparative Study of Three Different Topologies of Neural Network–based
Multiuser Detectors of WCDMA signals
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| Authors: | Tahani Abdalla Attia and Mohammed Ali H. Abbas
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| Abstract: | One of the main obstructions in Multi-Access systems
is the Multi-Access Interference (MAI) between signals
that sharing the same channel. Specially, in the CDMA
systems where all users share the same channel all the
time. The objective of this work is to compare three
different Artificial Neural Network (ANN)-based
multiuser detectors for Wideband Code Division
Multiple Access WCDMA system built to combat the
effect of the MAI. The system employs ANN
Multiuser Detectors (MUD) for a synchronous
Wideband Code Division Multiple Access (WCDMA)
mobile system uplink channel with variable number of
paths and complex-valued Rayleigh fading and uses a
bank of Rake receivers as pre-detectors. The three types
of the ANNs employed are the Backpropagation
FeedForward (FF), Radial Basis Function (RBF), and
The Self Organizing Feature Maps (SOM) which is an
unsupervised ANN involves clustering of patterns into
similar groups. Comparing results for the different
types of NN MUDs, the FFNN MUDs give better
performance when trained properly, but their training is
the main difficult issue and takes very long time. While
RBF MUDs’ training is easy and fast, attention is
needed to set the training parameters. On the other hand
SOM MUDs don’t need any training since they are
unsupervised NNs, but their implementation is not easy.
RBF and SOM MUDs give lower performance
compared to the FFNN MUDs.
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| Keywords: | Multiuser Detection, Neural Networks,
WCDMA, FFNN, RBF, SOM.
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| Journal/Conference: | International Journal of Artificial Intelligence and Machine Learning
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| Volume: | 6
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| Issue: | 3
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| Submission Date: | 7/1/2006 12:00:00 AM
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| Review Date: | 8/1/2006 12:00:00 AM
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| Publishing Date: | 9/1/2006 12:00:00 AM
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| Article Downloads: | 316
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