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Describes signals that cannot be precisely characterized.

Definitions, distributions, and stationarity

Stochastic Process
Given a sample space, a stochastic process is an indexed collection of random variables defined for each .
t t X t

Received signal at an antenna as in .

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For a given t , X t is a random variable with a distribution

First-order distribution

F X t b X t b X t b

First-order stationary process
If F X t b is not a function of time then X t is called a first-order stationary process.

Second-order distribution

F X t 1 , X t 2 b 1 b 2 X t 1 b 1 X t 2 b 2
for all t 1 , t 2 , b 1 , b 2

Nth-order distribution

F X t 1 , X t 2 , , X t N b 1 b 2 b N X t 1 b 1 X t N b N

N th-order stationary : A random process is stationary of order N if

F X t 1 , X t 2 , , X t N b 1 b 2 b N F X t 1 + T , X t 2 + T , , X t N + T b 1 b 2 b N

Strictly stationary : A process is strictly stationary if it is N th order stationary for all N .

X t 2 f 0 t where f 0 is the deterministic carrier frequency and : is a random variable defined over and is assumed to be a uniform random variable; i.e. , f 1 2 0

F X t b X t b 2 f 0 t b
F X t b 2 f 0 t b b 2 f 0 t
F X t b 2 f 0 t b 2 f 0 t 1 2 b 2 f 0 t 2 f 0 t 1 2 2 2 b 1 2
f X t x x 1 1 x 1 1 x 2 x 1 0
This process is stationary of order 1.

The second order stationarity can be determined by first considering conditional densities and the joint density. Recall that

X t 2 f 0 t
Then the relevant step is to find
X t 1 x 1 X t 2 b 2
Note that
X t 1 x 1 2 f 0 t x 1 2 f 0 t
X t 2 2 f 0 t 2 x 1 2 f 0 t 1 2 f 0 t 2 t 1 x 1

F X t 2 , X t 1 b 2 b 1 x 1 b 1 f X t 1 x 1 X t 1 x 1 X t 2 b 2
Note that this is only a function of t 2 t 1 .

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Every T seconds, a fair coin is tossed. If heads, then X t 1 for n T t n 1 T .If tails, then X t -1 for n T t n 1 T .

p X t x 1 2 x 1 1 2 x -1
for all t . X t is stationary of order 1.

Second order probability mass function

p X t 1 X t 2 x 1 x 2 p X t 2 X t 1 x 2 x 1 p X t 1 x 1

The conditional pmf

p X t 2 X t 1 x 2 x 1 0 x 2 x 1 1 x 2 x 1
when n T t 1 n 1 T and n T t 2 n 1 T for some n .
p X t 2 X t 1 x 2 x 1 p X t 2 x 2
for all x 1 and for all x 2 when n T t 1 n 1 T and m T t 2 m 1 T with n m
p X t 2 X t 1 x 2 x 1 0 x 2 x 1 for n T t 1 , t 2 n 1 T p X t 1 x 1 x 2 x 1 for n T t 1 , t 2 n 1 T p X t 1 x 1 p X t 2 x 2 n m for n T t 1 n 1 T m T t 2 m 1 T

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Source:  OpenStax, Intro to digital signal processing. OpenStax CNX. Jan 22, 2004 Download for free at http://cnx.org/content/col10203/1.4
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