Effects of random linear transformations on higher-order cyclostationary time-series

In many signal processing applications, it is advantageous to exploit the signal selectivity and noise tolerance that are typical of the algorithms based on the spectral line generation that often can be accomplished by using quadratic nonlinear transformations. The linear time-variant systems are classified as random and nonrandom in the fraction-of-time probability framework. Then, the way in which the higher-order wide-sense cyclostationarity properties of time-series change as they are processed by random or nonrandom linear systems is investigated.