Blind signal processing for impulsive noise channels

In this paper, a new blind signal processing scheme for equalization in fading and impulsive-noise channel environments is introduced based on probability density function matching method and a set of Dirac-delta functions. Gaussian kernel of the proposed blind algorithm has the effect of cutting out the outliers on the difference between the desired level values and impulse-infected outputs. And also the proposed algorithm has relatively less sensitivity to channel eigenvalue ratio and has reduced computational complexity compared to the recently introduced correntropy algorithm. According to these characteristics, simulation results show that the proposed blind algorithm produces superior performance in multi-path communication channels corrupted with impulsive noise.

[1]  D. Erdogmus,et al.  Convergence analysis of the information potential criterion in Adaline training , 2001, Neural Networks for Signal Processing XI: Proceedings of the 2001 IEEE Signal Processing Society Workshop (IEEE Cat. No.01TH8584).

[2]  H. Vincent Poor,et al.  Performance of DS/SSMA communications in impulsive channels. II. Hard-limiting correlation receivers , 1988, IEEE Trans. Commun..

[3]  Deniz Erdogmus,et al.  Entropy minimization for supervised digital communications channel equalization , 2002, IEEE Trans. Signal Process..

[4]  Haruo Ogiwara,et al.  Application of Turbo TCM Codes for Impulsive Noise Channel (Special Section on Information Theory and Its Applications) , 1998 .

[5]  Norihiko Morinaga,et al.  A Novel Receiver Design for DS-CDMA Systems under Impulsive Radio Noise Environments , 1999 .

[6]  Lee M. Garth,et al.  A dynamic convergence analysis of blind equalization algorithms , 2001, IEEE Trans. Commun..

[7]  Deniz Erdogmus,et al.  Stochastic blind equalization based on PDF fitting using Parzen estimator , 2005, IEEE Transactions on Signal Processing.

[8]  Guido Horst Bruck,et al.  Application of joint source-channel decoding to impulsive noise environments , 2004, IEEE 60th Vehicular Technology Conference, 2004. VTC2004-Fall. 2004.

[9]  Guy A. Dumont,et al.  The multimodulus blind equalization algorithm , 1997, Proceedings of 13th International Conference on Digital Signal Processing.

[10]  Deniz Erdogmus,et al.  An error-entropy minimization algorithm for supervised training of nonlinear adaptive systems , 2002, IEEE Trans. Signal Process..

[11]  Norihiko Morinaga,et al.  Receiver design using the dependence between quadrature components of impulsive radio noise , 1995, Proceedings IEEE International Conference on Communications ICC '95.

[12]  Jian Yang,et al.  The multimodulus blind equalization and its generalized algorithms , 2002, IEEE J. Sel. Areas Commun..

[13]  Deniz Erdogmus,et al.  A new classifier based on information theoretic learning with unlabeled data , 2005, Neural Networks.

[14]  Ian A. Glover,et al.  Measurement of the impulsive noise environment for satellite-mobile radio systems at 1.5 GHz , 2002, IEEE Trans. Veh. Technol..

[15]  Lee-Fang Wei.  Two-level coding based on trellis-coded modulation and Reed-Solomon codes , 1994, IEEE Trans. Commun..

[16]  Deniz Erdogmus,et al.  Information Theoretic Learning , 2005, Encyclopedia of Artificial Intelligence.

[17]  Kenneth S. Vastola,et al.  Threshold Detection in Narrow-Band Non-Gaussian Noise , 1984, IEEE Trans. Commun..

[18]  José Carlos Príncipe,et al.  Generalized correlation function: definition, properties, and application to blind equalization , 2006, IEEE Transactions on Signal Processing.

[19]  F. Mazzenga.  Channel estimation and equalization for M-QAM transmission with a hidden pilot sequence , 2000 .

[20]  Madhavendra Richharia,et al.  Satellite Communication Systems: Design Principles , 1999 .

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