Improved super-exponential algorithm for blind equalization

doi:10.1016/j.dsp.2007.10.009

Digital Signal Processing

Volume 19, Issue 3, May 2009, Pages 444-451

https://doi.org/10.1016/j.dsp.2007.10.009 Get rights and content

Abstract

Blind equalization is an effective way to suppress the inter-symbol interference (ISI), which is caused by multi-path fading communication channels. An improved super-exponential method (ISEM) is proposed here. The method, combining super-exponential method (SEM) and minimum description length (MDL), makes the equalization achieve good results even when the signal-to-noise ratio (SNR) is low. Compared with robust super-exponential method (RSEM) using fourth-order cumulants proposed by Kawamoto [M. Kawamoto, Robust super-exponential methods for blind equalization in the presence of Gaussian noise, IEEE Trans. Circuits Syst. II Express Briefs 52 (10) (2005) 651–655], the presented method has better robust performance but less computation.

Section snippets

Dr. Dan Wu was born in Heilongjiang province, China, in 1979. She received her master and Ph.D. degrees in Harbin Institute of Technology in 2004 and 2007, respectively. Her main research interests are satellite communication, blind detection, and so on.

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There are more references available in the full text version of this article.

Cited by (3)

Blind equalizer for constant-modulus signals based on Gaussian process regression
2012, Signal Processing
Citation Excerpt :
Since the equalization procedure requires the training sequences for convergence of the equalizer tap weights, the given bandwidth cannot be fully exploited. Therefore, the blind equalization procedure is the most popular strategy to overcome this inefficiency [1,2]. Recently, the machine learning techniques such as the relevance vector machine (RVM) [3] and support vector machine (SVM) [4,5] are applied to the blind equalization problems as well as to other communication problems [6–8].
A new blind equalization method for constant modulus (CM) signals based on Gaussian process for regression (GPR) by incorporating a constant modulus algorithm (CMA)-like error function into the conventional GPR framework is proposed. The GPR framework formulates the posterior density function for weights using Bayes' rule under the assumption of Gaussian prior for weights. The proposed blind GPR equalizer is based on linear-in-weights regression model, which has a form of nonlinear minimum mean-square error solution. Simulation results in linear and nonlinear channels are presented in comparison with the state-of-the-art support vector machine (SVM) and relevance vector machine (RVM) based blind equalizers. The simulation results show that the proposed blind GPR equalizer without cumbersome cross-validation procedures shows the similar performances to the blind SVM and RVM equalizers in terms of intersymbol interference and bit error rate.
Recent Advances in Neural Network Techniques for Channel Equalization: A Comprehensive Survey
2018, Proceedings - 2018 International Conference on Computing, Electronics and Communications Engineering, iCCECE 2018
New super-exponential iteration blind equalization algorithm for underwater acoustic communications
2011, Proceedings of 2011 International Conference on Image Analysis and Signal Processing, IASP 2011

Professor Xuemai Gu was born in Jiangsu province, China, in 1957. He received his master and Ph.D. degrees in Harbin Institute of Technology in 1985 and 1991, respectively. He is now the dean of Communication Research Center in Harbin Institute of Technology. His main research interests are data communication system, communication networks, and so on.

Professor Qing Guo was born in Heilongjiang province, China, in 1964. He received his master degree in Beijing University of Posts and Telecommunications in 1985 and doctor degree in Harbin Institute of Technology in 1990. His main research interests are command and control system transformation, wideband multimedia satellite communication system, and so on.

^☆: This work was supported in part by National 863 project of China (item number 2004AA001210).

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Improved super-exponential algorithm for blind equalization☆

Abstract

Section snippets

Two-stage interference-resistant adaptive periodically time-varying CMA blind equalization

IEEE Trans. Signal Process.

Fractionally spaced equalization using CMA: Robustness to channel noise and lack of disparity

IEEE Trans. Signal Process.

Subspace method for the blind identification of multi-channel FIR filters

IEEE Trans. Signal Process.

Blind identification of multi-output system using minimum noise subspace

IEEE Trans. Signal Process.

QR factorization based blind channel identification and equalization with second-order statistics

IEEE Trans. Signal Process.

Blind equalization using a tricepstrum-based algorithm

IEEE Trans. COM