Bit Error Rate Estimation
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Optical Communications Optical Bit Error Rate: An Estimation Methodology Stamatios V. Kartalopoulos ISBN: 978-0-471-61545-3 291 pages September
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2004, Wiley-IEEE Press Read an Excerpt Chapter (PDF)Preface (PDF)Table bit error rate calculator of Contents (PDF)Index (PDF) Description Optical Bit Error Rate: An Estimation Methodology provides an analytical methodology
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to the estimation of bit error rate of optical digital signals. This presents an extremely important subject in the design of optical communications systems and networks, yet http://ieeexplore.ieee.org/iel5/25/26755/01193111.pdf previous to the publication of this book the topic had not been covered holistically. The text lays out an easy-to-understand analytical approach to a highly important and complex subject: bit error rate (BER) estimation of a transmitted signal with a focus on optical transmission. It includes coverage of such important topics as impairments http://www.wiley.com/WileyCDA/WileyTitle/productCd-0471615455.html on DWDM optical signals, causes of signal distortion, and identification and estimation of the signal quality by statistical estimation of the bit error rate. The book includes numerous illustrations and examples to make a difficult topic easy to understand. This edition includes a CD-ROM with run-time simulations from a vendor that provides commercial software for the industry. See More See Less Table of Contents Preface. Acknowledgments. Constants, Conversions and Useful Formulae. Introduction. 1 Principles of Modulation and Digital Transmission. 1.1 Digital Versus Analog. 1.2 Spectrum in Optical Communications. 1.3 Linear Response to Square Input Pulses. 1.4 Principles of Modulation. 1.5 Modulator Types. 1.6 Principles of Decoding. 2 Optical Propagation. 2.1 Introduction. 2.2 The Wave Nature of Light. 2.3 Classical Interference. 2.4 Quantum Interference. 2.5 Light Attributes. 2.6 Matter. 2.7 Propagation of Light. 2.8 Diffraction. 2.9 Polarization. 2.10 Paradoxes. 2.11 Material Dispersion. 2.12 Glass Fiber, an Optical Transmission Medium. 2.13 Dispersion. 2.14 Fiber Polarization-Dependent Loss. 2.15 Self-Phase Modul
About Articles Submission Guidelines Research Article Open Access A Fast Soft Bit Error Rate Estimation MethodSamirSaoudi1, 2, 3Email author, ThomasDerham3, TarikAit-Idir4, 5 and PatriceCoupe3EURASIP Journal http://jwcn.eurasipjournals.springeropen.com/articles/10.1155/2010/372370 on Wireless Communications and Networking20102010:372370DOI: 10.1155/2010/372370© Samir Saoudi et al.2010Received: 9March2010Accepted: 30August2010Published: 5September2010 AbstractWe have suggested in a previous publication a method to estimate the Bit Error Rate (BER) of a digital communications system instead of using the famous Monte Carlo (MC) simulation. This method was based on the estimation of the probability density function (pdf) of soft error rate observed samples. The kernel method was used for the pdf estimation. In this paper, we suggest to use a Gaussian Mixture (GM) model. The Expectation Maximisation algorithm is used to estimate the parameters of this mixture. The optimal number of Gaussians is computed by using Mutual Information Theory. The analytical expression of the BER is therefore simply given by bit error rate using the different estimated parameters of the Gaussian Mixture. Simulation results are presented to compare the three mentioned methods: Monte Carlo, Kernel and Gaussian Mixture. We analyze the performance of the proposed BER estimator in the framework of a multiuser code division multiple access system and show that attractive performance is achieved compared with conventional MC or Kernel aided techniques. The results show that the GM method can drastically reduce the needed number of samples to estimate the BER in order to reduce the required simulation run-time, even at very low BER. 1. IntroductionTo study the performance of a digital communications system, we need to use, in general, the Monte Carlo (MC) method to estimate the BER. A tutorial exposition of different techniques is provided in [1] with particular reference to four other specific methods: modified Monte Carlo simulation (importance sampling), extreme value theory, tail extrapolation, and quasianalytical method. The modified Monte Carlo is achieved by importance sampling which means that important events, and then errors, are artificially generated by biasing the noise