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White noise draws its name from white light, [2] although light that appears white generally does not have a flat power spectral density over the visible band. An image of salt-and-pepper noise In discrete time , white noise is a discrete signal whose samples are regarded as a sequence of serially uncorrelated random variables with zero mean ...
Autocorrelation of white noise [ edit ] The autocorrelation of a continuous-time white noise signal will have a strong peak (represented by a Dirac delta function ) at τ = 0 {\displaystyle \tau =0} and will be exactly 0 {\displaystyle 0} for all other τ {\displaystyle \tau } .
A pseudo-noise code (PN code) or pseudo-random-noise code (PRN code) is one that has a spectrum similar to a random sequence of bits but is deterministically generated. The most commonly used sequences in direct-sequence spread spectrum systems are maximal length sequences, Gold codes, Kasami codes, and Barker codes. [4]
The term uncorrelated noise refers to a noise source being uncorrelated to a signal or another noise source. [1] White noise in particular, due to its randomness, is uncorrelated to any other signal and is also serially uncorrelated (i.e., later values of it have no correlation to earlier values).
In contrast, truly random sequence sources, such as sequences generated by radioactive decay or by white noise, are infinite (no pre-determined end or cycle-period). However, as a result of this predictability, PRBS signals can be used as reproducible patterns (for example, signals used in testing telecommunications signal paths).
Decorrelation is a general term for any process that is used to reduce autocorrelation within a signal, or cross-correlation within a set of signals, while preserving other aspects of the signal. [citation needed] A frequently used method of decorrelation is the use of a matched linear filter to reduce the autocorrelation of
In stochastic processes, chaos theory and time series analysis, detrended fluctuation analysis (DFA) is a method for determining the statistical self-affinity of a signal. It is useful for analysing time series that appear to be long-memory processes (diverging correlation time, e.g. power-law decaying autocorrelation function) or 1/f noise.
First, white noise is a generalized stochastic process with independent values at each time. [12] Hence it plays the role of a generalized system of independent coordinates, in the sense that in various contexts it has been fruitful to express more general processes occurring e.g. in engineering or mathematical finance, in terms of white noise.