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System Simulator > Noise WaveformsNoise is simply random fluctuations of a signal. This randomness is typically governed by a statistical distribution. For example, a White Gaussian noise process has a Gaussian distribution (i.e., the statistical distribution of the noise level obeys a Gaussian distribution). The noise processes supported by the Designer discrete time analysis are assumed stationary (i.e., the statistical distribution of the noise level does not vary with time). A baseband random noise signal may be represented in the following form:
where VN(t) is a time-varying noise voltage. A bandpass random noise signal, on the other hand, may be represented in the following form::
where fc is the carrier frequency. The quantity
is known as the complex envelope of the bandpass Noise N(t). IN(t) and QN(t) are the In-phase and Quadrature-phase baseband noise-bearing signals. Any noise process may be classified as uncorrelated or correlated. An uncorrelated noise process implies that the noise samples N(t) at time t and N(t + dt) at time t + dt are not correlated. A correlated process, on the other hand, implies that the samples N(t) and N(t + dt) tend to be correlated. An example of an uncorrelated process is White Gaussian noise. Examples of correlated processes are colored Gaussian noise and Rayleigh fading. The power spectral density of a noise process typically represents the correlation. For weakly correlated processes, this power spectral density tends to be wideband. A strongly correlated process, on the other hand, tends to have a narrowband power spectral density.
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