Home > Bit Error > Bit Error Rate Estimation Using Probability Density Function Estimators# Bit Error Rate Estimation Using Probability Density Function Estimators

## In addition, the Mean, the Standard deviation with the theoretical value of BER for different values of SNR are given in Table 3.

Authors’ Affiliations(1)Institut TELECOM, TELECOM Bretagne, UMR **CNRS 3192** Lab-STICC(2)Université Européenne de Bretagne, (UeB)(3)Institut TELECOM, TELECOM Bretagne, Technopôle Brest-Iroise CS 83818(4)Laboratoire CRISTAL, Ecole Nationale de Sciences de L'Informatique (ENSI), Campus Universitaire de Your cache administrator is webmaster. Proof of (14) Proof. Columbia University Press, New York, NY, USA; 1958.MATHGoogle ScholarAbedi A, Thompson ME, Khandani AK: Application of cumulant method in performance evaluation of turbo-like codes. http://performancepccanada.com/bit-error/bit-error-rate-estimation-for-turbo-decoding.php

Skip to main content Advertisement Menu **Search Search Search** Twitter Facebook Login to my account Publisher main menu Get published Explore Journals About Books EURASIP Journal on Wireless Communications and Networking Compute (19) 3.2. IEEE transactions on Communications Systems 1980, 28(11):1916-1924. 10.1109/TCOM.1980.1094613View ArticleGoogle ScholarGumbel EJ: Statistics of Extremes. Let us note that for conditional Gaussian distribution , and for Gaussian kernel, the optimal smoothing parameter is given by (3) 2.2.

The modular approach blurs the dynamics of layers interaction with the wireless medium, hindering the overall system performance with redundancy, inefficient resource handling and suboptimal performances. Performance EvaluationTo evaluate the performance of the three methods, we consider the framework of a synchronous CDMA system with two users using binary phase-shift keying (BPSK) and operating over an additive This is linked to the observed bias mentioned before.Table 4 Mean, Standard deviation and precision of BER estimation GM method, for SNR = 10 dB, at different number of samples are used

Details regarding the derivation of (15) are provided in Appendix D. 3.3. Dev.Precision1,000 0.2202,000 0.2205,000 0.22010,000 0.23215,000 0.22520,000 0.22725,000 0.22830,000 0.23035,000 0.24240,000 0.259100,000 submitted to IEEE Transactions on Communications ICC2011, 5–9 June, kyoto, JapanTitterington D, Smith A, Makov U: Statistical Analysis of Finite Mixture Distributions. Please try the request again.

We can show that for , the new parameters are given by: (see Appendices A–D for proofs) (12) (13) (14) 3.2. In this paper, we suggest a new means of estimating the BER. It is an iterative computation method of maximum likelihood estimates of missing data from observable variables. All the received soft output decisions are random variables having the same pdf, .Throughout the paper, the following notation is used.

The idea is to modify this pdf, that is, the statistical properties of the soft decision sequence, in such a way that higher rate of errors occur in the simulation process. Use of this web site signifies your agreement to the terms and conditions. If an accurate BER estimation can be performed in run time, the adaptation techniques can adjust the signal so as to guarantee lower than target BER on the link, which consequently Algorithm 1: Summary of the Gaussian Mixture based BER estimation using EM algorithm and Mutual Information theory. and are computed in a parallel fashion. 1.

At the crossroads of QoS-related constraints, devices complexity and efficient spectrum use, error control is indeed a key aspect of wireless communications - particularly crucial in the satellite context - where All these results are given in Table 4. We have carried out one hundred different trials and computed the Mean ISE (MISE) and the variance of the ISE for the three methods. Let us use the conditional Expectation of the log likelihood function given in Appendix A,(A.1).

For each pdf, a Gaussian Mixture model, with a large enough initial number of components, is used. weblink From top to bottom: Histogram, Kernel, and Gaussian methods. Samples are used. Consequently, computing time is reduced drastically. The Expectation Maximisation (EM) algorithm is used to estimate, in an iterative fashion, the different parameters of this mixture, that is, the means, the variances, and the a priori probabilities.

For this first simulation, we have chosen a pdf as a mixture of 3 different pdfs according to Gaussian, Rayleigh and Beta first kind laws with fixed different parameters. Using (22), we can easily show that the BEP for user is (23)In the following, we use the two spreading codes (24) (25) Where the cross-correlation is ReedRead moreArticleRobust GMSK demodulation using demodulator diversity and BER estimation [electronic resource] /October 2016J.D. navigate here Please try the request again.

HallLimited preview - 1993Common terms and phrasesBorel measurable Borel set Borel subsets bounded Cantor ternary set cardinality closed subset Consider continuous function continuum hypothesis converges Darboux function defined thereon disjoint element The number of EM iterations is fixed to . For simplicity reasons, we will not give the corresponding equations for the conditional pdf, .

BER Estimation Based on Gaussian Mixture Method In this section, we derive the expression of BER estimate assuming Gaussian Mixture based pdf estimator. When is a random variable, and denote the mathematical expectation and variance of , respectively. Missing data is given by unknown true component (Gaussian) from which the observation comes. Interestingly, we showed that while classical MC method fails to perform BER estimation in the region of high SNR, the proposed GM estimator provides reliable estimates and better, in the sense

rgreq-c17525e27b0f175d019d63362d719a85 false Skip to main content Advertisement Menu Search Search Search Twitter Facebook Login to my account Publisher main menu Get published Explore Journals About Books EURASIP Journal on Wireless Communications On the other hand, if this number is too high, this means that the same class of observed samples comes from different components and then these components should be correlated which The quasianalytical method combines noiseless simulation with analytical representation of noise. his comment is here Adaptive minimization of MSE does not always guarantee minimization of bit error rate (BER).

A component with positive MI is assumed to be dependent on others components and could be removed without damaging the pdf estimation. The CPU time for MC method at different values of SNR is given in Table 5.Table 5 CPU time for Monte Carlo simulation at different BER, assuming using computers with Therefore, the error count has to be modified appropriately to obtain an unbiased estimate of the true error probability. ISEHistogramKernelGaussian MixtureMean7.44 7.29 8.09 Std5.40 5.40 5.00 Table 2 Mean and Standard deviation of error estimation of using the three methods with 1,000 different

Register now for a free account in order to: Sign in to various IEEE sites with a single account Manage your membership Get member discounts Personalize your experience Manage your profile Adaptive filtering is the usual approach to mitigating this channel distortion. Bell System Technical Journal 1948, 27: 379-423, 623–656.MathSciNetView ArticleMATHGoogle ScholarVerdu S: Multiuser Detection. ERROR The requested URL could not be retrieved The following error was encountered while trying to retrieve the URL: http://0.0.0.7/ Connection to 0.0.0.7 failed.