Higher-order spectral analysis of complex signals

Even though higher-order spectral analysis is by now a mature field, complex signals are still not routinely used, as they are in second-order analysis. The reason is the complexity of the complex case: nth order moment functions of a complex signal can be defined in 2n different ways, depending on the placement of complex conjugate operators. It is demonstrated that only a few of these different moments are required for a complete nth order description. Properties of nth order moments and spectra with different conjugation patterns are investigated. For the special case of analytic signals, it is shown how spectra with different conjugation patterns provide different information about the signal. Both energy and power signals and deterministic and stochastic signals are discussed. A major focus lies on extending results from continuous-time signals to their sampled versions. Such an extension is not straightforward due to a phenomenon called higher-order or dimension-reduction aliasing. It is demonstrated why spectra of sampled nonstationary signals may suffer from dimension-reduction aliasing unless they are sufficiently oversampled.

[1]  George E. Ioup,et al.  Sampling requirements and aliasing for higher-order correlations , 1993 .

[2]  Bernard C. Picinbono,et al.  On circularity , 1994, IEEE Trans. Signal Process..

[3]  Boualem Boashash,et al.  The bispectral aliasing test , 1993, [1993 Proceedings] IEEE Signal Processing Workshop on Higher-Order Statistics.

[4]  Alfred Hanssen,et al.  A theory of polyspectra for nonstationary stochastic processes , 2003, IEEE Trans. Signal Process..

[5]  George E. Ioup,et al.  Sampling requirements for nth‐order correlations , 1994 .

[6]  Patrick Flandrin,et al.  Time-Frequency/Time-Scale Analysis , 1998 .

[7]  Antonio Napolitano,et al.  Higher-order cyclostationarity properties of sampled time-series , 1996, Signal Process..

[8]  William A. Gardner,et al.  The cumulant theory of cyclostationary time-series. II. Development and applications , 1994, IEEE Trans. Signal Process..

[9]  Giacinto Gelli,et al.  Blind widely linear multiuser detection , 2000, IEEE Communications Letters.

[10]  Jean-Louis Lacoume,et al.  Statistics for complex variables and signals - Part II: signals , 1996, Signal Process..

[11]  A. Shiryaev Some Problems in the Spectral Theory of Higher-Order Moments. I , 1960 .

[12]  Murray Wolinsky,et al.  The bispectral aliasing test: a clarification and some key examples , 1999, ISSPA '99. Proceedings of the Fifth International Symposium on Signal Processing and its Applications (IEEE Cat. No.99EX359).

[13]  Antonio Napolitano,et al.  Multirate processing of time series exhibiting higher order cyclostationarity , 1998, IEEE Trans. Signal Process..

[14]  Hagit Messer,et al.  On the principal domain of the discrete bispectrum of a stationary signal , 1995, IEEE Trans. Signal Process..

[15]  Louis L. Scharf,et al.  Detection and estimation of improper complex random signals , 2005, IEEE Transactions on Information Theory.

[16]  John W. Dalle Molle,et al.  Trispectral analysis of stationary random time series , 1995 .

[17]  William A. Gardner,et al.  The cumulant theory of cyclostationary time-series. I. Foundation , 1994, IEEE Trans. Signal Process..

[18]  Randolph L. Moses,et al.  The bispectrum of complex signals: definitions and properties , 1992, IEEE Trans. Signal Process..

[19]  James L. Massey,et al.  Proper complex random processes with applications to information theory , 1993, IEEE Trans. Inf. Theory.

[20]  Robert Schober,et al.  A novel iterative multiuser detector for complex modulation schemes , 2002, IEEE J. Sel. Areas Commun..

[21]  B. Harris Spectral Analysis Of Time Series , 1967 .

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

[23]  Michel Loève,et al.  Probability Theory I , 1977 .

[24]  Antonio Napolitano,et al.  Higher-order statistics for Rice's representation of cyclostationary signals , 1997, Signal Process..

[25]  Louis L. Scharf,et al.  Second-order analysis of improper complex random vectors and processes , 2003, IEEE Trans. Signal Process..

[26]  Melvin J. Hinich,et al.  A Test for Aliasing Using Bispectral Analysis , 1988 .

[27]  L. A. Pflug Principal domains of the trispectrum, signal bandwidth, and implications for deconvolution , 2000 .

[28]  Ananthram Swami,et al.  Pitfalls in polyspectra , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[29]  R. Field,et al.  Properties of higher‐order correlations and spectra for bandlimited, deterministic transients , 1992 .

[30]  Antonio Napolitano,et al.  Cyclic spectral analysis of continuous-phase modulated signals , 2001, IEEE Trans. Signal Process..

[31]  Jerry M. Mendel,et al.  Tutorial on higher-order statistics (spectra) in signal processing and system theory: theoretical results and some applications , 1991, Proc. IEEE.

[32]  William Gardner,et al.  Common Pitfalls in the Application of Stationary Process Theory to Time-Sampled and Modulated Signals , 1987, IEEE Trans. Commun..

[33]  M. Rosenblatt,et al.  Deconvolution and Estimation of Transfer Function Phase and Coefficients for NonGaussian Linear Processes. , 1982 .

[34]  Andrew T. Walden,et al.  Deconvolution, bandwidth, and the trispectrum , 1993 .

[35]  William A. Gardner,et al.  Cyclic Wiener filtering: theory and method , 1993, IEEE Trans. Commun..