As new communications services evolve, professionals must create better models to predict system performance. The article provides an overview of computer simulation modelling for communication networks, as well as some important related modelling issues. It gives an overview of discrete event simulation and singles out two important modelling issues that are germane to extant and emerging networks: traffic modelling and rare event simulation. Monte Carlo computer simulation is used as a performance prediction tool and Markov models are considered. >

Traffic modeling for telecommunications networks

Computer simulation is often used to estimate the bit error rate (BER) performance of digital communication systems. There are a number of distinct techniques in the simulation context that can be used to construct this estimate. A tutorial exposition of such techniques is provided, with particular reference to five specific methods which can be implemented in a simulation. These methods range from the traditional Monte Carlo trials to assumption of definite forms for the noise statistics. An attempt is made to show how these methods are related, and the specific assumptions that are invoked in order to apply them.

Techniques for Estimating the Bit Error Rate in the Simulation of Digital Communication Systems

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.

Quick simulation: a review of importance sampling techniques in communications systems

https://nottingham-repository.worktribe.com/preview/985347/Review%20and%20application%20of%20Artificial%20Neural%20Networks%20models%20in%20reliability%20analysis%20of%20steel%20structures.pdf

Review and application of Artificial Neural Networks models in reliability analysis of steel structures

The phase noise associated with single-mode semiconductor lasers must be accounted for in performance studies of lightwave communication systems. The standard phase noise model is a Brownian-motion stochastic process. Although many analyses of lightwave communication systems have been published, none, to the authors knowledge, has fully adhered to the standard model. The reason is that a proper characterization of filtered lightwave signal had not been achieved. Such a characterization, along with theoretical approaches to obtaining it, is detailed. The authors show, for example, how to generate probability density functions (PDFs) of the magnitude of a filtered laser tone (with special attention to the tail region) and how to analytically represent the characteristic function of the PDF in closed form in the small-phase-noise realm. With the characterization in place, the stage is now set for determining the bit-error rate performance of advanced detection techniques which seek to mitigate the phase noise impairment. >

Characterizing filtered light waves corrupted by phase noise

Digital communication systems are frequently operated over nonlinear channels with memory. The analysis of the performance of these systems is difficult and no complete analytical treatment of the problem has been obtained before. Several recent efforts have been directed toward the computation of error probabilities via Monte-Carlo simulation using a complete system model. These simulations require excessively large sample sizes and are not practical for estimating very low values of error probabilities. This paper presents a modified Monte-Carlo simulation technique for estimating error probabilities in digital communication systems operating over nonlinear channels. An importance-sampling technique is used to modify the probability density function of the noise process in a way to make simulation possible. Theoretical results as well as realistic examples are presented, showing that the number of samples needed for simulation is reduced considerably.

K. S. Shanmugam

Papers

A Modified Monte-Carlo Simulation Technique for the Evaluation of Error Rate in Digital Communication Systems