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

Abstract

Abstruct-Digital communication systemsare 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.

Probability of detection models for eddy current NDE methods

“…Importance Sampling Technique [ 43] The estimation of the parameters such as POD and PFA for a given flaw, basically involves the integrations of the tail regions of distributions.…”

“…For instance from equation ( 4.5) the probability of false alarm is expressed as PFA = f fy(y) dy (6.1) where fy(y) is the noise PDF and T the threshold value selected. The number of samples, N, required to estimate this integration using the Monte-Carlo simulation technique is given by where £is the normalized error in the estimated PF A and is given by Importance sampling technique [ 43] consists on transforming the density function fy(y) to a PDF (y{y) such that the probability of a sample occurring in the tail region in the new distribution is higher. This is illustrated in Figure 6.3.…”

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

“…The simulation techniques have their origin in Monte Carlo simulation (MCS) method, which generates a large sample set of limit state evaluations and approximates the true value of the probability of failure by = , where is the number of samples lying in the failure region and S the total number of samples. In order to further improve the computational efficiency of MCS, many variance reduction techniques have been proposed [9], including importance sampling ( [10], [11]), directional simulation [12] or subset simulation ( [13], [14]). Despite these improvements, the MCS method is still timeconsuming and further development is crucial.…”

Efficient BER simulation of orthogonal space-time block codes in Nakagami-m fading

“…Although the application of IS is well documented, the search for a convenient biased distribution remains a major issue in many papers, e.g., [2]- [5]. In order to speed up the simulation of orthogonal space-time block codes (OSTBCs) on i.i.d.…”