Cyclostationarity: half a century of research
Signal Processing
Bibliography on cyclostationarity
Signal Processing
A conjugate-cyclic-autocorrelation projection-based algorithm for signal parameter estimation
EURASIP Journal on Wireless Communications and Networking
Estimation of the symbol rate of linearly modulated sequences of symbols
Signal Processing
Discrete-time estimation of second-order statistics of generalized almost-cyclostationary processes
IEEE Transactions on Signal Processing
EURASIP Journal on Advances in Signal Processing - Special issue on dynamic spectrum access for wireless networking
Wireless Personal Communications: An International Journal
IEEE Transactions on Wireless Communications
Two-dimensional harmonic retrieval in correlative noise based on genetic algorithm
EURASIP Journal on Advances in Signal Processing - Special issue on robust processing of nonstationary signals
Higher-order cyclic cumulants for high order modulation classification
MILCOM'03 Proceedings of the 2003 IEEE conference on Military communications - Volume I
A cumulant based algorithm for the identification of input-output quadratic systems
Automatica (Journal of IFAC)
Hi-index | 754.84 |
We generalize Parzen's (1961) analysis of “asymptotically stationary” processes to mixtures of deterministic, stationary, nonstationary, and generally complex time series. Under certain mixing conditions expressed in terms of joint cumulant summability, we show that time averages of such mixtures converge in the mean-square sense to their ensemble averages. We additionally show that sample averages of arbitrary orders are jointly complex normal and provide their covariance expressions. These conclusions provide us with statistical tools that treat random and deterministic signals on a common framework and are helpful in defining generalized moments and cumulants of mixed processes. As an important consequence, we develop consistent and asymptotically normal estimators for time-varying, and cyclic-moments and cumulants of kth-order cyclostationary processes and provide computable variance expressions. Some examples are considered to illustrate the salient features of the analysis