A basic and generally accepted noise model is known as Additive White Gaussian Noise (AWGN), which imitates various random processes seen in nature. Let’s break each of those words down for further clarity:
Additive – As its name suggests, noise is added to a signal. In other words, the signal that’s received equates to the signal that’s transmitted…plus some noise. Moreover, the noise is generated randomly and has an individual probability from the signal, meaning the occurrence of one does not affect the probability of occurrence of the other.
White – This refers to the idea that the noise has the same power distribution at every frequency. Therefore, white noise has a constant Power Spectral Density (the measure of a signal’s power compared to frequency) across all frequencies. You may be asking, “Why choose the word ‘white’ to represent this idea?” Well, if I focused a beam of light for each color on the visible spectrum onto a single spot, that combination would result in a beam of white light. As a result, white light is comprised of a combination of all colors or frequencies in the visible spectrum.
Gaussian – Due to a noise source’s random nature, a mathematical model is used to calculate the probability of events. Gaussian Distribution, or a normal distribution, has an average of zero in the time domain, and is represented as a bell-shaped curve that is symmetrical about the mean with no left or right bias.
The random nature of noise can distort signals and the integrity of electrical systems. Therefore, noise generators can help measure a system’s response to noise, using an AWGN channel to introduce an average number of errors through the system. So just remember, a little bit of a perceived “bad” occurrence can be used to help bolster designs in the long run.