Adaptive Importance Sampling Technique for Neural Detector Training

Authors:
José L. Sanz-González;Francisco Álvarez-Vaquero
Affiliations:
-;-
Venue:
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Year:
2002

Citing 4
Cited 0

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Importance Sampling Techniques in Neural Detector Training

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Abstract

In this paper, we develop the use of an adaptive Importance Sampling (IS) technique in neural network training, for applications to detection in communication systems. Some topics are reconsidered, such as modifications of the error probability objective function (Pe), optimal and suboptimal IS probability density functions (biasing density functions), and adaptive importance sampling. A genetic algorithm was used for the neural network training, having utilized an adaptive IS technique for improving Pe estimations in each iteration of the training. Also, some simulation results of the training process are included in this paper.