Importance Sampling Techniques in Neural Detector Training

Authors:
José L. Sanz-González;Diego Andina
Affiliations:
-;-
Venue:
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Year:
2001

Citing 4
Cited 2

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
A stochastic importance sampling methodology for the efficient simulation of adaptive systems in frequency nonselective Rayleigh fading channels

IEEE Journal on Selected Areas in Communications

Adaptive Importance Sampling Technique for Neural Detector Training

ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Importance Sampling for Objective Function Estimations in Neural Detector Training Driven by Genetic Algorithms

Neural Processing Letters

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Abstract

Importance Sampling is a modified Monte Carlo technique applied to the estimation of rare event probabilities (very low probabilities). In this paper, we propose and develop the use of Importance Sampling (IS) techniques in neural network training, for applications to detection in communication systems. Some key topics are introduced, such as modifications of the error probability objective function, optimal and suboptimal IS probability density functions (biasing density functions), and experimental results of training with a genetic algorithm. Also, it is shown that the genetic algorithm with the IS technique attains quasi-optimum training in the sense of minimum error probability (or minimum misclassification probability).