Title :
Probabilistic neural networks for multi-user detection in code divisional multiple access communication channels
Author :
Ibikunle, Frank ; Zhong, Y.X.
Author_Institution :
Beijing Univ. of Posts & Telecommun., China
Abstract :
A probabilistic neural network is proposed and applied for implementation of a maximum likelihood detector and classifier. The network is trained using the algorithm based on Parzen probability density function estimation theory for detection of signals in CDMA multi-user communications Gaussian channel. By viewing these multi-user detector´s problem as a nonlinear classification decision problem, we apply this algorithm which has the abilities of arbitrary nonlinear transformations, adaptive learning and tracking to implement this decision optimally and adaptively. The performance of the proposed neural networks detector is evaluated via extensive computer simulations and compared with other detectors and neural classifiers´ schemes in a multi-user environment. The neural detector is shown to exhibits some desirable properties and significantly outperforms the conventional matched filter detector
Keywords :
code division multiple access; maximum likelihood estimation; neural nets; pattern classification; probability; signal detection; telecommunication channels; Parzen probability density function; adaptive learning; code divisional multiple access communication; communication channels; estimation theory; maximum likelihood detector; multiple user detection; pattern classifier; probabilistic neural network; signal detection; Detectors; Estimation theory; Gaussian channels; Maximum likelihood detection; Maximum likelihood estimation; Multiaccess communication; Multiuser detection; Neural networks; Probability density function; Signal detection;
Conference_Titel :
Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on
Conference_Location :
Anchorage, AK
Print_ISBN :
0-7803-4859-1
DOI :
10.1109/IJCNN.1998.687265