Importance Sampling Techniques in Neural Detector Training

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).KeywordsGenetic AlgorithmError ProbabilityImportance SamplingCommunication DetectorNeural Network TrainingThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.