MindMap Gallery Communication Principles-Random Process
Detailed introduction to stationary random processes, Gaussian random processes, Stationary random processes via linear systems, Gaussian white noise, band-limited white noise, etc.
Edited at 2024-02-18 11:22:05Explore the fascinating world of limits, a fundamental concept in calculus that underpins derivatives and integrals. This overview delves into the core idea of limits, emphasizing how they describe the value a function approaches as the input nears a certain point. Learn about intuitive understandings through approaches versus equals, and the formal ε–δ definition that rigorously defines limits. Discover various types of limits, including one-sided and limits at infinity, and when limits exist or fail. Uncover key properties, their relationship to continuity, and techniques for evaluating limits. Join us in mastering the foundational concepts that shape mathematical analysis!
Explore the fundamental concepts of work and power, essential for understanding energy dynamics in physics. This overview covers core definitions, including work as energy transfer and power as the rate of work done. Delve into the work-energy relation, examining the work-kinetic energy theorem and the distinctions between conservative and nonconservative forces. Learn how to calculate work under various conditions, from constant forces to variable forces and multiple interactions. The mechanical energy framework explains energy conservation principles, while power calculations provide insight into energy transfer rates. Utilize graphical tools and diagrams to visualize these concepts, avoiding common pitfalls in understanding work and its implications.
Discover the fascinating world of isotopes, the variants of chemical elements that share the same number of protons but differ in neutrons, leading to unique properties. This overview covers the core definitions and atomic structure basics of isotopes, including their notation and abundance. Learn about examples like hydrogen, carbon, and oxygen, and differentiate between stable isotopes and radioisotopes. Understand the significance of isotopic variation, its origins in stellar processes and fractionation, and how we measure isotopes using advanced techniques like mass spectrometry. Join us in exploring the critical role isotopes play in science and nature.
Explore the fascinating world of limits, a fundamental concept in calculus that underpins derivatives and integrals. This overview delves into the core idea of limits, emphasizing how they describe the value a function approaches as the input nears a certain point. Learn about intuitive understandings through approaches versus equals, and the formal ε–δ definition that rigorously defines limits. Discover various types of limits, including one-sided and limits at infinity, and when limits exist or fail. Uncover key properties, their relationship to continuity, and techniques for evaluating limits. Join us in mastering the foundational concepts that shape mathematical analysis!
Explore the fundamental concepts of work and power, essential for understanding energy dynamics in physics. This overview covers core definitions, including work as energy transfer and power as the rate of work done. Delve into the work-energy relation, examining the work-kinetic energy theorem and the distinctions between conservative and nonconservative forces. Learn how to calculate work under various conditions, from constant forces to variable forces and multiple interactions. The mechanical energy framework explains energy conservation principles, while power calculations provide insight into energy transfer rates. Utilize graphical tools and diagrams to visualize these concepts, avoiding common pitfalls in understanding work and its implications.
Discover the fascinating world of isotopes, the variants of chemical elements that share the same number of protons but differ in neutrons, leading to unique properties. This overview covers the core definitions and atomic structure basics of isotopes, including their notation and abundance. Learn about examples like hydrogen, carbon, and oxygen, and differentiate between stable isotopes and radioisotopes. Understand the significance of isotopic variation, its origins in stellar processes and fractionation, and how we measure isotopes using advanced techniques like mass spectrometry. Join us in exploring the critical role isotopes play in science and nature.
stochastic process
basic concept
definition
The set of all sample functions ζi(t)
The set of random variables ξi(t)
Attributes
It has the characteristics of both random variables and time functions.
Feature description
Distribution function
digital features
Distribution function
Statistical properties that describe isolated moments
One-dimensional
Distribution function
Probability density function
two-dimensional
Distribution function
Probability density function
n dimensions
Distribution function
Probability density function
The larger the dimension n, the more fully the statistical characteristics of the random process are described.
digital features
mean
swing center
Determining function of t
variance
degree of deviation
When a(t)=0, σ^2(t)=E[ζ^2(t)]
related functions
autocorrelation function
degree of correlation in the same process
When τ=t2-t1, then R(t1,t2)=R(t1,t2 τ)
cross-correlation function
The degree of correlation between the two processes
stationary random process
definition
Stable in a narrow sense
The statistical properties of random processes have nothing to do with the starting point of time
Generalized stationary
The mean has nothing to do with time t: a(t)=a
relation
Ergodicity (ergodicity)
significance
Statistical average = time average
meaning
Any sample has experienced all possible states of the stationary process
autocorrelation function
important properties
average power
DC power
AC power (variance)
even function
Upper Bound
Power spectral density PSD
The power spectrum of the sample
Process power spectrum
nature
The PSD of any sample in the traversal process = the PSD of the process
Non-negativity:
Even function:
Gaussian random process
Gaussian process
definition
If any n-dimensional (n=1,2,...) distribution of the random process ζ(t) obeys the normal distribution, it is called a Gaussian process
important properties
If it is stationary in the broad sense, then it is stationary in the narrow sense.
If they are not related to each other, they are statistically independent.
The algebraic sum of several Gaussian processes is still Gaussian.
Gaussian process → linear transformation → Gaussian process
definition
If any n-dimensional (n =1,2,...) distribution of the random process ζ(t) obeys the normal distribution, it is called a normal process or Gaussian process.
The n-dimensional normal probability density function is expressed as
important properties
The n-dimensional distribution of the Gaussian process only relies on the mean, variance and normalized covariance of each random variable.
A generalized stationary Gaussian process is also strictly stationary
If the values of Gaussian processes at different times are uncorrelated, then they are also statistically independent.
The process generated by a Gaussian process after linear transformation is still a Gaussian process. It can also be said that if the input of a linear system is a Gaussian process, the system output is also a Gaussian process.
Gaussian random variable
One-dimensional probability density function
Recorded as (a, σ2) a---distribution center σ---concentration degree
image
nature
Symmetric about the straight line x=a
normal distribution function
error function
Increasing function of independent variables erfc(0)=0 erfc(∞)=1
The concise nature of the error function helps analyze the noise immunity of communication systems
complementary error function
Decreasing function of independent variables erfc(0)=1 erfc(∞)=0
Stationary stochastic processes through linear systems
Gaussian white noise and band-limited white noise
White Noise
ideal broadband process
definition
autocorrelation function
Gaussian white noise
Refers to white noise whose probability distribution obeys Gaussian distribution
The values of Gaussian white noise at any two different times are not only unrelated to each other, but also statistically independent.
band limited white noise
White noise passing through a channel or filter with limited bandwidth
common forms
White noise through LPF - low pass white noise
White noise through BPF - bandpass white noise
low pass white noise
Power Spectral Density
autocorrelation function
band pass white noise
Power Spectral Density
autocorrelation function
average power
Sine wave plus narrowband Gaussian noise
Generalized Rayleigh distribution, also known as Rician distribution
narrowband random process
expression
envelope phase form
In-phase quadrature form
The relationship between the two
Statistical properties
Statistical properties of in-phase and quadrature components
Statistical properties of envelope and phase