NDLI: An algorithmic approach to the optimization of importance sampling parameters in digital communication system simulation

Content Provider	IEEE Xplore Digital Library
Author	Devetsikiotis, M. Townsend, J.K.
Copyright Year	1972
Abstract	Importance sampling is recognized as a potentially powerful method for reducing simulation runtimes when estimating the bit error rate (BER) of communications systems using Monte Carlo simulation. Analytically, minimizing the variance of the importance sampling (IS) estimator with respect to the biasing parameters has typically yielded solutions for systems for which the BER could be found analytically. A technique for finding an asymptotically optimal set of biasing parameter values, in the sense that as the resolution of the search and the number of runs used both approach infinity, the algorithm converges to the true optimum, is proposed. The algorithm determines the amount of biasing that minimizes a statistical measure of the variance of the BER estimate and exploits a theoretically justifiable relationship, for small sample sizes, between the BER estimate and the amount of biasing. The translation biasing scheme is considered, although the algorithm is applicable to other parametric IS techniques. Only mild assumptions are required of the noise distribution and system. Experimentally, improvement factors ranging from two to eight orders of magnitude are obtained for a number of distributions for both linear and nonlinear systems with memory.<>
Starting Page	1464
Ending Page	1473
Page Count	10
File Size	1086129
File Format	PDF
ISSN	00906778
Volume Number	41
Issue Number	10
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1993-10-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Monte Carlo methods Bit error rate Runtime Communication systems Signal processing algorithms Digital communication Sampling methods Analysis of variance Linear systems Additive noise
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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