Bandpass sampling criteria for nonlinear systems

Sampling criteria for nonlinear systems with a band-pass input are developed in this paper. It is well known that nonlinear systems may produce an output signal with a larger bandwidth than that of their input signal. According to the Nyquist sampling theorem, the sampling rate needs to be at least twice the maximum frequency of the output signal; otherwise, the sampled output would be aliased. However, if the input is a bandpass signal, the spectrum of the output signal often occupies multiple frequency bands. In this case, it is possible, by using the bandpass sampling concept, to sample the output signal at a rate much lower than the Nyquist sampling frequency. In this paper, all conditions in which bandpass sampling can be achieved are derived for nonlinear systems up to the third order. Furthermore, for nonlinear systems higher than the third order, some conditions in which bandpass sampling can be guaranteed are derived. The result can be used to choose an appropriate sampling frequency for nonlinear systems of an arbitrary order.

[1]  D. Brillinger The identification of polynomial systems by means of higher order spectra , 1970 .

[2]  Taiho Koh,et al.  Second-order Volterra filtering and its application to nonlinear system identification , 1985, IEEE Trans. Acoust. Speech Signal Process..

[3]  William H. Tranter,et al.  Efficient Simulation of Multicarrier Digital Communication Systems in Nonlinear Channel Environments , 1993, IEEE J. Sel. Areas Commun..

[4]  W. Rugh Nonlinear System Theory: The Volterra / Wiener Approach , 1981 .

[5]  Dimitris Pavlidis,et al.  Mechanisms determining third order intermodulation distortion in AlGaAs/GaAs heterojunction bipolar transistors , 1992 .

[6]  I. Sandberg The mathematical foundations of associated expansions for mildly nonlinear systems , 1983 .

[7]  Alan V. Oppenheim,et al.  Discrete-Time Signal Pro-cessing , 1989 .

[8]  Ching-Hsiang Tseng,et al.  A mixed-domain method for identification of quadratically nonlinear systems , 1997, IEEE Trans. Signal Process..

[9]  Sergio Benedetto,et al.  Nonlinear Equalization of Digital Satellite Channels , 1982, IEEE J. Sel. Areas Commun..

[10]  Nikolaos Zervos,et al.  Adaptive nondisruptive measurement of harmonic distortion for voiceband data transmission , 1995, IEEE Trans. Commun..

[11]  M. Schetzen The Volterra and Wiener Theories of Nonlinear Systems , 1980 .

[12]  Ching-Hsiang Tseng Identification of cubically nonlinear systems using undersampled data , 1997 .

[13]  Sang-Won Nam,et al.  Application of higher order spectral analysis to cubically nonlinear system identification , 1994, IEEE Trans. Signal Process..

[14]  Peter Grant,et al.  DIGITAL COMMUNICATIONS , 2022 .

[15]  John Tsimbinos,et al.  Input Nyquist sampling suffices to identify and compensate nonlinear systems , 1998, IEEE Trans. Signal Process..

[16]  R. J. Martin,et al.  Volterra system identification and Kramer's sampling theorem , 1999, IEEE Trans. Signal Process..

[17]  Edward J. Powers,et al.  A digital method of modeling quadratically nonlinear systems with a general random input , 1988, IEEE Trans. Acoust. Speech Signal Process..

[18]  W. Frank Sampling requirements for Volterra system identification , 1996, IEEE Signal Processing Letters.

[19]  Rodney G. Vaughan,et al.  The theory of bandpass sampling , 1991, IEEE Trans. Signal Process..

[20]  R. Tymerski,et al.  Volterra series modelling of power conversion systems , 1990, 21st Annual IEEE Conference on Power Electronics Specialists.

[21]  J. Bendat New techniques for nonlinear system analysis and identification from random data , 1990 .