lysis tween noise components in different frequency bands. This is precisely what a cy- clostationary noise model has to offer. However, before elaborating cyclostationary processes in depth, we first give a brief review on traditional stationary noise theory as it contains many of its results will be needed further on. 3.5.3.2 A brief review on stationary noise theory Stationary noise processes [Mid87] are the type of stochastic processes most common to designers. Mathematically, a zero-mean stochastic process n(t) is (wide-sense) sta- tionary if its autocorrelation function does not depend on the time t, or E{n(t)n(t-t)"}=Rmm(t) (3.124) The way in which the behavior of the noise process at time t interrelates with that at a past or future time instance t - T, only depends on the time difference T. As was already explained, this comes down to the absence of cross-correlations between noise components in different (non-overlapping) frequency bands. Many noise sources that occur in practice are stationary, e.g. the ones associated with a transistor ¹0. A special case of stationary noise is white noise. For a zero-mean white noise process nw(t), it holds that E {nw(t)nw(t-t)*}=N(t) = (3.125) where S(T) is the Dirac delta function. This implies that there is no correlation what- soever between samples of a white noise process taken at different instances in time. Non-white noise processes are also said to be colored. Stationarity turns out to be a most convenient property in dealing with a noise process n(t). Frequency-domain manipulation becomes possible by introducing the noise process power spectral density (PSD) function Snn(jw). This PSD function is the time-averaged energy content in a narrow frequency band around W, or Snn(jw) (3.126) From the definition it is immediately clear that Snn(jw) is real and positive to E R. The lower equality follows from the Wiener-Khinchin theorem [Mid87]. Note that for a white noise source (jw) = N, to Use of the PSD function Snn(jw) makes it very easy to compute changes in noi