An introduction to signal detection and estimation (2nd ed.)
An introduction to signal detection and estimation (2nd ed.)
Performance Analysis of Neural Network Detectors by Importance Sampling Techniques
Neural Processing Letters
Quick simulation: a review of importance sampling techniques in communications systems
IEEE Journal on Selected Areas in Communications
IEEE Journal on Selected Areas in Communications
Advances in Neyman-Pearson Neural Detectors Design
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
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This letter deals with the use of Importance Sampling (IS) techniques and the Mean-Square (MS) error in neural network training, for applications to detection in communication systems. Topics such as modifications of the MS objective function, optimal and suboptimal IS probability density functions, and adaptive importance sampling are presented. A genetic algorithm was used for the neural network training, having considered adaptive IS techniques for improving MS error estimations in each iteration of the training. Also, some experimental results of the training process are shown in this letter. Finally, we point out that the mean-square error (estimated by importance sampling) attains quasi-optimum training in the sense of minimum error probability (or minimum misclassification error).