The Spectra Of A Random Process With Variance Shift Keying B White

The Spectra Of A Random Process With Variance Shift Keying B White Abstract — this paper investigates the possibility of real life using the brand new type of digital modulation, which implies the transmission of white gaussian noise whose variance changes in time. that modulation has been entitled as variance shift keying. This paper investigates the possibility of real life using the brand new type of digital modulation, which implies the transmission of white gaussian noise whose variance changes in time.

Shift A Trend B And Variance C Modiied White Noise Time Series For random processes, representation theorem leads to random signals being described by random vectors with uncorrelated components. theorem of irrelavance allows us to disregrad nearly all components of noise in the receiver. We can write s[x, λ] = α2 s[x, 0], where s[x, 0] = 2σ2 α. α2 λ2 the correlation time for this process is z ∞ τcor = |k(τ)| dτ = α−1 k(0) 0 as α → ∞, σ2 = s[x, 0]α 2 → ∞ and τcor → 0 (i.e. the process becomes white noise). White gaussian noise can be described as the "derivative" of brownian motion. brownian motion is an important random process that will be discussed in the next chapter. What if the sequence x(n) is a wide sense stationary (wss) process? in this case we usually use capital letter x(n) to indicate that it is a stochastic process.

Shift A Trend B And Variance C Modified White Noise Time Series White gaussian noise can be described as the "derivative" of brownian motion. brownian motion is an important random process that will be discussed in the next chapter. What if the sequence x(n) is a wide sense stationary (wss) process? in this case we usually use capital letter x(n) to indicate that it is a stochastic process. One of the most important characteristics of a random process is its auto correlation function, which leads to the spectral information of the random process. the frequency content process depends on the rapidity of the am plitude change with time. The spectrum of white noise is constant over a broad frequency band. this is in analogy with white light that contains light of all colors over the frequency band of visible light. From example 19, we have seen that a wss random process can be created by filtering white noise with an lsi filter that has a system function h(z). the system function of the filter, h(z), was found by performing a spectral factorization. Describing the characteristics of random process is very much needed in the design of communication experiments. autocorrelation function, cross correlation function, and covariance are used to describe the statistical properties of random processes in time domain.