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Nexxim Simulator >
Nexsys Discrete Time Domain Analysis >
   Overview of Discrete Time Domain Analysis >
       Signal and Noise Waveforms           


Signal and Noise Waveforms

The signals and waveforms present in a system are Baseband Signals, Bandpass Signals, and Noise Waveforms

Baseband Signals

The general form of any baseband signal may be represented in the following form:

where VS(t) represents a time-varying voltage signal.

Bandpass Signals

A baseband signal becomes a bandpass signal once it modulates an RF carrier. The general form of any bandpass signal is:

Where fc is the carrier frequency and is the phase of the input modulated signal.

The quantity

is known as the complex envelope of the bandpass signal S(t). IS(t) and QS(t) are the In-phase and Quadrature-phase baseband information-bearing signals.

Noise Waveforms

Noise 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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