Blind Non-Minimum Phase Channel Identification Using 3rd and 4th Order Cumulants

In this paper we propose a family of algorithms based on 3 rd and 4 th order cumulants for blind single-input single-output (SISO) Non-Minimum Phase (NMP) Finite Impulse Response (FIR) channel estimation driven by non-Gaussian signal. The input signal represents the signal used in 10GBASE-T (or IEEE 802.3an-2006) as a Tomlinson-Harashima Precoded (THP) version of random Pulse-Amplitude Modulation with 16 discrete levels (PAM-16). The proposed algorithms are tested using three non-minimum phase channel for different Signal-to-Noise Ratios (SN R) and for different data input length. Numerical simulation results are presented to illustrate the performance of the proposed algorithms. Keywords—Higher Order Cumulants, Channel identification, Ethernet communication.

[1]  Georgios B. Giannakis,et al.  Cumulant-based autocorrelation estimates of non-Gaussian linear processes , 1995, Signal Process..

[2]  Keang-Po Ho,et al.  Spectral efficiency limits and modulation/detection techniques for DWDM systems , 2004, IEEE Journal of Selected Topics in Quantum Electronics.

[3]  ANANTHRAM SWAMI,et al.  Closed-form recursive estimation of MA coefficients using autocorrelations and third-order cumulants , 1989, IEEE Trans. Acoust. Speech Signal Process..

[4]  K. V. S. Hari,et al.  FIR system identification using higher order cumulants-a generalized approach , 1995, IEEE Trans. Signal Process..

[5]  P. Loubaton,et al.  Blind second-order identification of FIR channels: forced cyclostationarity and structured subspace method , 1997, IEEE Signal Process. Lett..

[6]  Jitendra K. Tugnait,et al.  New results on FIR system identification using higher-order statistics , 1990 .

[7]  A. Zeroual,et al.  Blind Identification in Noisy Environment of Nonminimum Phase Finite Impulse Response (FIR) System Using Higher Order Statistics , 2003 .

[8]  John G. Proakis,et al.  Digital Communications , 1983 .

[9]  Brian M. Sadler,et al.  Estimation and detection in non-Gaussian noise using higher order statistics , 1994, IEEE Trans. Signal Process..

[10]  M. Tsatsanis,et al.  Stochastic maximum likelihood methods for semi-blind channel estimation , 1998, IEEE Signal Processing Letters.

[11]  Steve McLaughlin,et al.  MA parameter estimation and cumulant enhancement , 1996, IEEE Trans. Signal Process..

[12]  A. Zeroual,et al.  Ma system identification using higher order cumulants application to modelling solar radiation , 2002 .

[13]  M. Tsatsanis,et al.  Stochastic maximum likelihood methods for semi-blind channel equalization , 1997, Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136).

[14]  Jitendra K. Tugnait,et al.  Approaches of FIR system identification with noisy data using higher order statistics , 1990, IEEE Trans. Acoust. Speech Signal Process..

[15]  Xianda Zhang,et al.  FIR system identification using higher order statistics alone , 1994, IEEE Trans. Signal Process..

[16]  Georgios B. Giannakis,et al.  Noisy input/output system identification using cumulants and the Steiglitz-McBride algorithm , 1996, IEEE Trans. Signal Process..

[17]  Zhenya He,et al.  Criteria and algorithms for blind source separation based on cumulants , 1996 .

[18]  Said Safi,et al.  Blind parametric identification of non-Gaussian FIR systems using higher order cumulants , 2004, Int. J. Syst. Sci..

[19]  Sheng Chen,et al.  A clustering technique for digital communications channel equalization using radial basis function networks , 1993, IEEE Trans. Neural Networks.

[20]  Georgios B. Giannakis,et al.  Wireless multicarrier communications , 2000, IEEE Signal Process. Mag..

[21]  C. L. Nikias,et al.  Signal processing with higher-order spectra , 1993, IEEE Signal Processing Magazine.

[22]  M. Shafi,et al.  An improved optical heterodyne DPSK receiver to combat laser phase noise , 1995 .