 stationary colored noise process can be obtained by passing a white noise signal through an appropriate LTI filter. Such a filter is also called an LTI operator. Hence, the class of stationary colored noise processes is generated by the algebra of LTI operators. Here, the word 3.5.3 Noise analysis "algebra" simply denotes the fact that connecting two LTI operators in series or in parallel again yields an LTI operator. The result above suggests us to define a class of noise processes that is generated by LPTV operators: Definition (Cyclostationary noise): A noise process n(t) is called cyclostationary if there exists an LPTV operator H[.] with kernel h(t,T) such that (3.135) where nw(t) is a white noise process. In words, cyclostationary noise is obtained by passing white noise through an LPTV system. Since the set of all LPTV systems makes up an operator algebra, LPTV system behav- ior will, by definition, preserve cyclostationarity. Cyclostationary noise processes are therefore associated with LPTV systems just as stationary noise processes are associ- ated with LTI systems. It should be noted that, traditionally, cyclostationarity is defined by means of the pe- riodicity of the autocorrelation function Rnn(t,T) with respect to t [Royc98, Strom77]. However, the definition above gives a better insight into the nature of cyclostation- ary noise: it relates the structure of a noise process to that of a "generating" operator. Furthermore, this definition is readily generalized: for each algebra of LTV operators, there exists a corresponding class of noise processes. 3.5.3.4 Characterizing cyclostationary noise Consistent with the ideas underlying the HTM framework, we choose to represent a cyclostationary noise signal n(t) as a vector n_1(t) n(t)= (3.136) of stochastic equivalent baseband components. The overall noise process n(t) then equals = e-jwot ] . (3.137) This equality holds in a mean square sense. The processes nk(t) can be interpreted as the noisy signal content in the k-th frequency band11. Up to second 