Statistics for complex variables and signals—Part II: signals
Signal Processing
Higher-order cyclostationarity properties of sampled time-series
Signal Processing
Multirate processing of time series exhibiting higher ordercyclostationarity
IEEE Transactions on Signal Processing
Cyclic spectral analysis of continuous-phase modulated signals
IEEE Transactions on Signal Processing
Second-order analysis of improper complex random vectors and processes
IEEE Transactions on Signal Processing
On the principal domain of the discrete bispectrum of a stationarysignal
IEEE Transactions on Signal Processing
A theory of polyspectra for nonstationary stochastic processes
IEEE Transactions on Signal Processing
Detection and estimation of improper complex random signals
IEEE Transactions on Information Theory
A novel iterative multiuser detector for complex modulation schemes
IEEE Journal on Selected Areas in Communications
Correlation and spectral methods for determining uncertainty in propagating discontinuities
IEEE Transactions on Signal Processing
On the asymptotic distribution of GLR for impropriety of complex signals
Signal Processing
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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.