Gaussian noise - definition
Gaussian noise - definition
Gaussian noise is a type of random noise whose sample values have a statistical distribution described by a Gaussian probability density function (normal distribution). This means that most of the values are concentrated around the mean, with the probability of extreme values decreasing symmetrically as they move away from the mean. Noise of this type is widely used in the analysis of communication systems, signal processing and interference modelling in measurement and transmission systems.
A characteristic feature of Gaussian noise is its description by two parameters: the mean (usually equal to zero) and the variance, which defines the spread of values around the mean and thus the intensity of the noise. Although it can occur in different domains (time, space, frequency), its statistical nature remains constant. In contrast to white noise, which is defined by a flat spectrum, Gaussian noise refers to the amplitude distribution of a signal, regardless of its spectral properties. However, in practice, Gaussian-distributed white noise is often considered, i.e. a random process with a flat spectrum and a normal distribution of samples.
Gaussian noise is a fundamental model in information theory and telecommunications, forming the basis of the so-called Additive White Gaussian Noise (AWGN) channel, in which random noise with a white spectrum and normal distribution is assumed to be added to the useful signal. This model allows the analytical study of the effect of noise on the reliability of data transmission, filter design, correction coding and signal detection.
In practical applications, Gaussian noise can represent thermal disturbances in electronic systems, measurement fluctuations in analogue sensors or random errors in digital images. Its knowledge and modelling skills are crucial in the analysis of system quality and in the design of signal detection, estimation and filtering algorithms.
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