Effect of Different BER Performance Comparison of MAP and ML Detection
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Effect of Different BER Performance Comparison of MAP and ML Detection

Authors: Naveed Ur Rehman, Rehan Jamil, Irfan Jamil

Abstract:

In this paper, we regard as a coded transmission over a frequency-selective channel. We plan to study analytically the convergence of the turbo-detector using a maximum a posteriori (MAP) equalizer and a MAP decoder. We demonstrate that the densities of the maximum likelihood (ML) exchanged during the iterations are e-symmetric and output-symmetric. Under the Gaussian approximation, this property allows to execute a one-dimensional scrutiny of the turbo-detector. By deriving the analytical terminology of the ML distributions under the Gaussian approximation, we confirm that the bit error rate (BER) performance of the turbo-detector converges to the BER performance of the coded additive white Gaussian noise (AWGN) channel at high signal to noise ratio (SNR), for any frequency selective channel.

Keywords: MAP, ML, SNR, Decoder, BER, Coded transmission.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1099780

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References:


[1] J. H. Winters and R. D. Gitlin, \Electrical signal processing techniques in long-haul ber-optic systems," IEEE Trans. Comm., vol. 38, no. 9, pp. 1439{1453, Sep. 1990.
[2] H. Bulow, \Electronic equalization of transmission impairments," in Proc. OFC 2002, Anaheim, CA, TuF.
[3] T. Adal_, \Signal processing for optical communications," in Proc. 15th LEOS, Glasgow, Scotland, 2002.
[4] H. Bulow and G. Thielecke, \Electronic PMD mitigation - from linear equalization to maximum likelihood detection," in Proc. OFC 2001, Anaheim, CA, WAA3. 6
[5] H. F. Haunstein, K. Sticht, A. Dittrich, W. Sauer-Gre, and R. Urbansky, \Design of near optimum electrical equalizers for optical transmission in the presence of PMD," in Proc. OFC 2001, Anaheim, CA, WAA4.
[6] R. Holzlohner,V. S. Grigoryan, C. R. Menyuk, and W. L. Kath, \Accurate calculation of eye diagrams and bit error rates in optical transmission systems using linearization," J. Lightwave Tech., vol. 20, pp. 389{400, March 2002.
[7] Wenze Xi, T. Adal, and J. Zweck, \A MAP equalizer for the optical communications channel," submitted to J. Lightwave Tech., June 2004.
[8] L. R. Bahl, J. Cocke, F. Jelinek, and J. Raviv, “Optimal decoding of linear codes for minimizing symbol error rate,” IEEE Trans. Inf. Theory, vol. IT-32, pp. 284–287, Mar. 1974.
[9] S. Benedetto and E. Biglieri, Principles of Digital Transmission With Wireless Applications. New York: Kluwer/Plenum, 1999.
[10] C. Berrou, A. Glavieux, and P. Thitimajshima, “Near Shannon limit error-correcting coding and decoding: Turbo codes,” in Proc. IEEE Int. Conf. Communications, May 1993, pp. 1064–1070.
[11] C. Douillardat et al., “Iterative correction of intersymbol interference: Turbo-equalization,” Eur. Trans. Telecommun., vol. 6, no. 5, pp. 507–511, 1995.
[12] G. D. Forney, Jr., “Maximum-likelihood sequence estimation for digital sequences in the presence of inter symbol interference,” IEEE Trans. Inf. Theory, vol. 18, pp. 363–378, May 1972.
[13] Bracewell, R. (2000). The Fourier Transform and its applications. McGraw-Hill Science/Engineering/Math, 3 edition.
[14] Lees, J. M. and Park, J. (1995). Multiple-taper spectral analysis: A stand-alone C-subroutine. Computers & Geosciences, 21(2):199–236.
[15] Parker, R. L. and Barbour, A. J. (2013). psd: Adaptive, sine-multitaper power spectral density estimation. R package version 0.4-0.
[16] Percival, D. and Walden, A. (1993). Spectral analysis for physical applications. Cambridge University Press.
[17] Rahim, K. and Burr, W. (2013). multitaper: Multitaper Spectral Analysis. R package version 1.0-7.
[18] Denis Cousineau, Sebastien Helie, “Improving maximum likelihood estimation using prior probabilities: A tutorial on maximum a posteriori estimation and an examination of the weibull distribution” Tutorials in Quantitative Methods for Psychology, 2013, Vol. 9(2), p. 61-71.
[19] Bishop, C.M. Neural Networks for Pattern Recognition, NY: Oxford University Press, Inc. (1995).
[20] Edwards W, Lindman H & Savage L J. Bayesian statistical inference for psychological research. Psychol. Rev. 70:193-242, 1963
[21] Jeffreys, H. Theory of Probability, Third Edition . Glasgow: Oxford University Press: 1961;
[22] Hastie, T. Tibshirani, R. and Friedman, J. The elements of statistical learning: Data mining, Inference and Prediction, New York: springer, 2001.