An Approximated Expression for the Residual ISI Obtained by Blind Adaptive Equalizer and Biased Input Signals
- 1 Department of Electrical and Electronic Engineering, Ariel University, Ariel, Israel
- 2 Department of Electrical and Electronic Engineering, Ariel University, Ariel, Israel
Abstract
Recently, two expressions (for the noiseless and noisy case) were proposed for the residual inter-symbol interference (ISI) obtained by blind adaptive equalizers, where the error of the equalized output signal may be expressed as a polynomial function of order 3. However, those expressions are not applicable for biased input signals. In this paper, a closed-form approximated expression is proposed for the residual ISI applicable for the noisy and biased input case. This new proposed expression is valid for blind adaptive equalizers, where the error of the equalized output signal may be expressed as a polynomial function of order 3. The new proposed expression depends on the equalizer’s tap length, input signal statistics, channel power, SNR, step-size parameter and on the input signal’s bias. Simulation results indicate a high correlation between the simulated results and those obtained from our new proposed expression.
- Pinchas, M. (2013) Two Blind Adaptive Equalizers Connected in Series for Equalization Performance Improvement. Journal of Signal and Information Processing, 4, 64-71. http://dx.doi.org/10.4236/jsip.2013.41008
- Pinchas, M. (2013) Residual ISI Obtained by Blind Adaptive Equalizers and Fractional Noise. Mathematical Problems in Engineering, 2013, Article ID: 972174.
- Pinchas, M. and Bobrovsky, B.Z. (2006) A Maximum Entropy Approach for Blind Deconvolution. Signal Processing (Eurasip), 86, 2913-2931. http://dx.doi.org/10.1016/j.sigpro.2005.12.009
- Pinchas, M. and Bobrovsky, B.Z. (2007) A Novel HOS Approach for Blind Channel Equalization. IEEE Transactions on Wireless Communications, 6, 875-886. http://dx.doi.org/10.1109/TWC.2007.04404
- Pinchas, M. (2009) Blind Equalizers by Techniques of Optimal Non-Linear Filtering Theory. VDM Verlagsservice gesellschaft mbH.
- Pinchas, M. (2012) The Whole Story behind Blind Adaptive Equalizers/Blind Deconvolution. e-Books Publications Department, Bentham Science Publishers, Sharjah.
- Godard, D.N. (1980) Self Recovering Equalization and Carrier Tracking in Two-Dimensional Data Communication System. IEEE Transactions on Communications, 28, 1867-1875. http://dx.doi.org/10.1109/TCOM.1980.1094608
- Im, G.H., Park, C.J. and Won, H.C. (2009) A Blind Equalization with the Sign Algorithm for Broadband Access. IEEE Communications Letters, 5, 70-72.
- Reuter, M. and Zeidler, J.R. (1999) Nonlinear Effects in LMS Adaptive Equalizers. IEEE Transactions on Signal Processing, 47, 1570-1579. http://dx.doi.org/10.1109/78.765126
- Makki, A.H.I., Dey, A.K. and Khan M.A. (2010) Comparative Study on LMS and CMA Channel Equalization. 2010 International Conference on Information Society (i-Society), London, 28-30 June 2010, 487-489.
- Tucu, E., Akir, F. and Ozen, A. (2013) A New Step-Size Control Technique for Blind and Non-Blind Equalization Algorithms. Radioengineering, 22, 44-51.
- Wang, J.F. and Zhang, B. (2010) Design of Adaptive Equalizer Based on Variable Step LMS Algorithm. Proceedings of the 3rd International Symposium on Computer Science and Computational Technology, Jiaozuo, 14-15 August 2010, 256-258. http://www.academypublisher.com/proc/iscsct10/papers/iscsct10p256.pdf
- Nikias, C.L. and Petropulu, A.P. (1993) Chapter 9. Higher-Order Spectra Analysis A Nonlinear Signal Processing Framework. Prentice-Hall, Englewood Cliffs, 419-425.