A constant modulus algorithm for blind equalization in α-stable noise

Abstract Channel noise is often assumed to be Gaussian in most of the existing channel equalization algorithms. The performance of these algorithms will degrade seriously when the noise is non-Gaussian. This paper deals with the problem of blind channel equalization in impulsive noise environment that is modeled as α -stable process. A modified adaptive error-constrained constant modulus algorithm (MAECCMA) is proposed by soft-limiting the amplitude of the equalizer input and transforming the error signal of the original adaptive error-constrained constant modulus algorithm (AECCMA) nonlinearly to suppress the influence of α -stable noise. Computer simulation results of two underwater acoustic channels show that, MAECCMA has almost the same performance as AECCMA and they both have faster convergence rate than constant modulus algorithm (CMA) and normalized least mean absolute deviation (NLMAD) algorithm in Gaussian noise, while MAECCMA provides the best performance of those four algorithms in α -stable noise.

[1]  Tianshuang Qiu,et al.  Direction finding in non-Gaussian impulsive noise environments , 2007, Digit. Signal Process..

[2]  C. L. Nikias,et al.  Signal processing with fractional lower order moments: stable processes and their applications , 1993, Proc. IEEE.

[3]  Adam Zielinski,et al.  Performance analysis of digital acoustic communication in a shallow water channel , 1995, IEEE Journal of Oceanic Engineering.

[4]  Tianshuang Qiu,et al.  Underwater sources location in non-Gaussian impulsive noise environments , 2006, Digit. Signal Process..

[5]  Chrysostomos L. Nikias,et al.  Symmetric alpha-stable filter theory , 1997, IEEE Trans. Signal Process..

[6]  R. Adler,et al.  A practical guide to heavy tails: statistical techniques and applications , 1998 .

[7]  A. Cetin,et al.  Robust adaptive filtering algorithms for /spl alpha/-stable random processes , 1999 .

[8]  D. Middleton.  Statistical-Physical Models of Urban Radio-Noise Environments - Part I: Foundations , 1972 .

[9]  Enrico Del Re,et al.  Robust constant modulus arrays based on fractional lower-order statistics , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[10]  A. Enis Çetin,et al.  Adaptive filtering approaches for non-Gaussian stable processes , 1995, 1995 International Conference on Acoustics, Speech, and Signal Processing.

[11]  Daesik Hong,et al.  Adaptive error-constrained method for LMS algorithms and applications , 2005, Signal Process..

[12]  Yongbin Wei,et al.  Noise-constrained least mean squares algorithm , 2001, IEEE Trans. Signal Process..

[13]  J. Treichler,et al.  A new approach to multipath correction of constant modulus signals , 1983 .

[14]  H. Vincent Poor,et al.  Robust multiuser detection in non-Gaussian channels , 1999, IEEE Trans. Signal Process..

[15]  M. Harisankar,et al.  Simulated Performance of a DS Spread-Spectrum System in Impulsive Atmospheric Noise , 1987, IEEE Transactions on Electromagnetic Compatibility.

[16]  D. Godard,et al.  Self-Recovering Equalization and Carrier Tracking in Two-Dimensional Data Communication Systems , 1980, IEEE Trans. Commun..

[17]  S. Nishifuji,et al.  On-line sensing system of dynamic ship’s attitude by use of servo-type accelerometers , 1995, IEEE Journal of Oceanic Engineering.

[18]  Stuart C. Schwartz,et al.  Comparison of adaptive and robust receivers for signal detection in ambient underwater noise , 1989, IEEE Trans. Acoust. Speech Signal Process..

[19]  Milica Stojanovic,et al.  Recent advances in high-speed underwater acoustic communications , 1996 .

[20]  Enrico Del Re,et al.  Constant modulus blind equalization based on fractional lower-order statistics , 2004, Signal Process..