Design of the equaliser with lower-order Chebyshev polynomial for bandwidth expansion of transmission channel of logging cable

ABSTRACT This paper introduces the logging cable equaliser. In logging engineering, the seven-core cable has been widely used as transmission medium, which has a narrower effective bandwidth. In order to improve the data transfer rate, it is necessary to expand the effective bandwidth. We have proposed a design method of equaliser based on Chebyshev polynomial in this paper. This scheme could compensate the high-order channel attenuation and expand the effective width with lower-order Chebyshev polynomials. The equaliser could also compensate the phase shift of the transfer data which is caused by the non-linear characteristic of logging cable. We have also simulated the scheme with Matlab and produced the circuit board to demonstrate the practical application effect. It is proved that for the non-linear logging transmission channel, the proposed scheme has a good effect on the expansion of the effective bandwidth. It could provide enough bandwidth to enhance the data transmission rate.

[1]  Hussein Baher,et al.  Design of Analog Filters , 2012 .

[2]  Jiashu Zhang,et al.  Functional link neural network cascaded with Chebyshev orthogonal polynomial for nonlinear channel equalization , 2008, Signal Process..

[3]  C.F.N. Cowan,et al.  Adaptive equalization of finite nonlinear channels using multilayer perceptron , 1990 .

[4]  A. Worapishet,et al.  5th-Order Chebyshev Acitive-RC Complex Filter Employing Current Amplifiers , 2007, 2007 International Symposium on Integrated Circuits.

[5]  Zhang Yi,et al.  A unified learning algorithm to extract principal and minor components , 2009, Digit. Signal Process..

[6]  Yi-Gang He,et al.  A neural network approach to FIR filter design using frequency-response masking technique , 2008, Signal Process..

[7]  Kerim Demirbas A novel real-time adaptive suboptimal recursive state estimation scheme for nonlinear discrete dynamic systems with non-Gaussian noise , 2012, Digit. Signal Process..

[8]  S. Siu,et al.  Decision feedback equalisation using neural network structures and performance comparison with standard architecture , 1990 .

[9]  Narasimha H. Ayachit,et al.  Design of a fifth-order FIR digital differentiator using modified weighted least-squares technique , 2010, Digit. Signal Process..

[10]  J.C. Patra,et al.  Nonlinear channel equalization with QAM signal using Chebyshev artificial neural network , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

[11]  Jacek Piskorowski A new concept of phase-compensated continuous-time Chebyshev filters , 2008, Signal Process..

[12]  Yue-Dar Jou,et al.  Eigenfilter design of linear-phase FIR digital filters using neural minor component analysis , 2014, Digit. Signal Process..

[13]  G. Panda,et al.  Functional Link Artificial Neural Network for Active Control of Nonlinear Noise Processes , 2003 .

[14]  Ravi Sankar,et al.  Theoretical derivation of minimum mean square error of RBF based equalizer , 2007, Signal Process..

[15]  Yue-Dar Jou,et al.  WLS design of FIR Nyquist filter based on neural networks , 2011, Digit. Signal Process..

[16]  A. Doucet,et al.  Monte Carlo Smoothing for Nonlinear Time Series , 2004, Journal of the American Statistical Association.

[17]  Zhang Yi,et al.  Convergence analysis of a simple minor component analysis algorithm , 2007, Neural Networks.

[18]  Ravi Sankar,et al.  Theoretical Derivation of Minimum Mean Square Error of RBF Based Equalizer , 2006, ICNC.

[19]  Alfonso Fernández-Vázquez,et al.  Generalized Chebyshev Filters for the Design of IIR Filters and Filter Banks , 2014, Circuits Syst. Signal Process..

[20]  Marco F. Huber Chebyshev polynomial Kalman filter , 2013, Digit. Signal Process..

[21]  Wai-Kai Chen,et al.  Linear Networks and Systems , 1983 .

[22]  Sheng Chen,et al.  Adaptive Equalisation to finite Non-linear Channels using Multilayer Perceptrons , 1990 .

[23]  Yue-Dar Jou,et al.  Design of Hilbert transformer and digital differentiator using a neural learning algorithm , 2012, 2012 International Symposium on Intelligent Signal Processing and Communications Systems.