Importance Sampling and Mean-Square Error in Neural Detector Training
Neural Processing Letters
Importance Sampling Techniques in Neural Detector Training
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Adaptive Importance Sampling Technique for Neural Detector Training
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
A self-learning worm using importance scanning
Proceedings of the 2005 ACM workshop on Rapid malcode
An adaptive approach to fast simulation of traffic groomed optical networks
WSC '04 Proceedings of the 36th conference on Winter simulation
Importance sampling techniques for estimating the bit error rate in digital communication systems
WSC '05 Proceedings of the 37th conference on Winter simulation
An adaptive approach to accelerated evaluation of highly available services
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Optimal worm-scanning method using vulnerable-host distributions
International Journal of Security and Networks
A POPULATION MONTE CARLO METHOD FOR GENERATING RANDOM MATRICES WITH KNOWN CHARACTERISTICS
Applied Artificial Intelligence
Robust reconfigurable filter design using analytic variability quantification techniques
Proceedings of the 2008 IEEE/ACM International Conference on Computer-Aided Design
Low BER performance estimation of LDPC codes via application of importance sampling to trapping sets
IEEE Transactions on Communications
Tail extrapolation in MLSE receivers using nonparametric channel model estimation
IEEE Transactions on Signal Processing
Adaptive sampling for efficient failure probability analysis of SRAM cells
Proceedings of the 2009 International Conference on Computer-Aided Design
Monte Carlo simulation with error classification for multipath Rayleigh fading channel
ICT'09 Proceedings of the 16th international conference on Telecommunications
Monte Carlo simulation with error classification for QAM modulation under Rayleigh fading channel
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
Evaluation of the extremely low block error rate of irregular LDPC codes
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
IEEE Journal on Selected Areas in Communications
Regular {4,8} LDPC codes and their low error floors
MILCOM'06 Proceedings of the 2006 IEEE conference on Military communications
Issues in simulation and modelling of communication systems
ICOSSE'06 Proceedings of the 5th WSEAS international conference on System science and simulation in engineering
Artificial metaplasticity and the challenge to train ANNS with reduced pattern availability
CIMMACS'07 Proceedings of the 6th WSEAS international conference on Computational intelligence, man-machine systems and cybernetics
SIAM Journal on Scientific Computing
Computing highly accurate or exact P-values using importance sampling
Computational Statistics & Data Analysis
Active/passive combination-type performance measurement method using change-of-measure framework
Computer Communications
Improved cross-entropy method for estimation
Statistics and Computing
Zero-Variance Importance Sampling Estimators for Markov Process Expectations
Mathematics of Operations Research
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Importance sampling (IS) is a simulation technique which aims to reduce the variance (or other cost function) of a given simulation estimator. In communication systems, this usually, but not always, means attempting to reduce the variance of the bit error rate (BER) estimator. By reducing the variance, IS estimators can achieve a given precision from shorter simulation runs; hence the term “quick simulation.” The idea behind IS is that certain values of the input random variables in a simulation have more impact on the parameter being estimated than others. If these “important” values are emphasized by sampling more frequently, then the estimator variance can be reduced. Hence, the basic methodology in IS is to choose a distribution which encourages the important values. This use of a “biased” distribution will, of course, result in a biased estimator if applied directly in the simulation. However, there is a simple procedure whereby the simulation outputs are weighted to correct for the use of the biased distribution, and this ensures that the new IS estimator is unbiased. Hence, the “art” of designing quick simulations via IS is entirely dependent on the choice of biased distribution. Over the last 50 years, IS techniques have flourished, but it is only in the last decade that coherent design methods have emerged. The outcome of these developments is that at the expense of increasing technical content, modern techniques can offer substantial run-time saving for a very broad range of problems. We present a comprehensive history and survey of IS methods. In addition, we offer a guide to the strengths and weaknesses of the techniques, and hence indicate which techniques are suitable for various types of communications systems. We stress that simple approaches can still yield useful savings, and so the simulation practitioner as well as the technical researcher should consider IS as a possible simulation tool