Download Random Process Sample Space S S

Random Process
The concept of random variable was defined previously as mapping from the
Sample Space S to the real line as shown below
Sample Space
S
sn
sn 1
sn  2
sn 1
xn  2
xn 1
xn
xn 1
The concept of random process can be extended to include time
and the outcome will be random functions of time as shown below
The functions
xn  2 (t ), xn 1 (t ), xn (t ), xn 1 (t ),
are one realizations
of many of the random process X(t)
A random process also represents a random variable when time is fixed
X(t1 )
is a random variable
Stationary and Independence
The random process X(t) can be classified as follows:
First-order stationary
A random process is classified as first-order stationary if its first-order
probability density function remains equal regardless of any shift in time
to its time origin.
If we Xt1let represent a given value at time t1
then we define a first-order stationary as one that satisfies the
following equation:
f X (x t1 ) = f X (x t1 + τ)
The physical significance of this equation is that our density function,
f X (x t1 )
is completely independent of t1 and thus any time shift t
For first-order stationary the mean is a constant, independent of
any time shift
Second-order stationary
A random process is classified as second-order stationary if its
second-order probability density function does not vary over any
time shift applied to both values.
In other words, for values Xt1 and Xt2 then we will have the following
be equal for an arbitrary time shift t
f X (x t1 ,x t2 ) = f X (x t1+τ ,x t2+τ )
From this equation we see that the absolute time does not affect
our functions, rather it only really depends on the time difference
between the two variables.
For a second-order stationary process, we need to look at the
autocorrelation function ( will be presented later) to see its most
important property.
Since we have already stated that a second-order stationary process
depends only on the time difference, then all of these types of processes
have the following property:
R XX (t,t+τ) = E[X(t)X(t+τ)]
= R XX (τ)
Wide-Sense Stationary (WSS)
A process that satisfies the following:
E  X(t) = X = constant
E  X(t)X(t + τ) = R XX (τ)
is a Wide-Sense Stationary (WSS)
Second-order stationary
The converse is not true in general
Wide-Sense Stationary
Time Average and Ergodicity
Ergodicity
An attribute (‫ ) سمة‬of stochastic systems; generally, a system that tends
in probability to a limitingform that is independent of the initial
condititions
The time average of a quantity is defined as
1 T
A  = lim
 dt


T   2T  T
Here A is used to denote time average in a manner analogous to
E for the statistical average.
The time average is taken over all time because, as applied to
random processes, sample functions of processes are presumed
to exist for all time.
Let x(t) be a sample of the random process X(t) were the lower case
letter imply a sample function (not random function).
X(t) the random process
x(t) a sample
of the random
process the
random
process
Let x(t) be a sample of the random process X(t) were the lower
case letter imply a sample function.
We define the mean value x = A  x(t)
( a lowercase letter is used to imply a sample function)
and the time autocorrelation function XX (τ) as follows:
1 T
x = A  x(t)  = lim
x(t) dt

T   2T  T
XX (τ) = A  x(t)x(t + τ)
1 T
= lim
x(t)x(t + τ) dt


T
T   2T
For any one sample function ( i.e., x(t) ) of the random process X(t),
the last two integrals simply produce two numbers.
A number for the average x
and a number for XX (τ) for a specific value of t
Since the sample function x(t) is one out of other samples functions
of the random process X(t),
The average x
and the autocorrelation
are actually random variables
By taking the expected value for
x
XX (τ)
and XX (τ) ,we obtain
1 T

  lim 1 T E[x(t)] dt
E[x] = E[A[x(t)]] = E  lim
x(t) dt  T  

T
T   2T  T
2T


1 T
X(1) = X
 lim
X dt = Tlim



T   2T  T
1 T


E[XX (τ)] = E [A[x(t)x(t + τ)] ] = E  lim
x(t)x(t
+
τ)
dt

T   2T  T

1 T
1 T
= lim
E[x(t)x(t + τ)] dt = lim
R XX (τ) dt = R XX (τ)
T   2T  T
T   2T  T
Correlation Function
Autocorrelation Function and Its Properties
The autocorrelation function of a random process X(t) is the correlation
E  X1X 2 
of two random variables
X1 = X(t1 ) and X 2 = X(t 2 )
by the process at times t1 and t2
R XX (t1 ,t 2 ) = E  X(t1 )X(t 2 )
Assuming a second-order stationary process
R XX (t, t + τ) = E  X(t)X(t + τ)
R XX (τ) = E  X(t)X(t + τ)
Properties of
(1)
R XX (τ)  R XX (0)
R XX (τ)
autocorrelation function is bounded by
its value at the origin
(2)
R XX (  τ) = R XX (τ)
autocorrelation function is an even function
(3)
R XX (0) = E  X 2 (t) 
The average power in the process
(4)
E  X (t )  X  0
lim R XX (τ) = X 2
τ 
and X  t  is ergodic with no periodic components then
Example 6.3 -1
Let X(t) be a stationary ergodic process with autocorrelation function RXX (t ) given as RXX (t )  25 
4
1  6t 2
Covariance Function
The Covariance of tworandom variables X and Y was defined as
C XY = 11 = E ( X  X )(Y  Y )  =

 

 
( x  X )( y  Y ) f XY ( x, y)dxdy
We extend the concept of of covariance to random process
C XX (t , t  t )  E  X (t )  E  X (t )  X (t  t )  E (t  t ) 
which can be put in the form
C XX (t , t  t )  RXX (t , t  t )  E  X (t )  E  X (t  t ) 
which is similar to the two random variables case
For WSS stationary process X  t  , we get CXX (τ) = R XX (τ)  X 2
 C XX (0)  RXX (0)  X 2  E[ X 2 (t )]  X 2   X 2
which is the variance of X  t 
Example 6.3 -1
Let X(t) be a stationary ergodic process with autocorrelation function RXX (t ) given as RXX (t )  25 
4
1  6t 2
6.4 Measurement of Correlation Functions
In the real word , we can never measure the true correlation function of random process
because we never have all samples functions
6.5 Gaussian Random Processes
The Gaussian Random Processes is the most important one used in describing many
physical processes
Let X  t  be a Gaussian Random Processes as shown
Define N random variables X1  X (t1 ),
, X i  X (ti ),
, X N  X (t N )
We call the process a Gaussian if for any N  1,2 the joint density function is given as
C X 
1 1/2
f X1 ,
XN
(x1 ,
xN ) =
(2 ) N /2
  x  X  t C 1  x  X  

 X 

exp  

2


where  x  X  and C X  are defined next
Joint Gaussian density
C X 
1 1/2
f X1 ,
XN
(x1 ,
 x1
x
x 2


 xN
xN ) =
(2 ) N /2
  x  X  C   x  X  

 X 

exp  

2




 random vector



C1N 
 C11 C12
C
C22
C2 N 
21

C X   



C
C
C
N2
NN 
 N1
t
1
 X1 
 
X
X   2  mean vector
 
 
XN 
  transpose
1
  matrix inverse
t
 x1 - X 1 


x
X
2 
 x  X    2




x
X
N
 N
covariance matrix
 E[( X i  X i ) 2
Cij  E[( X i  X i )( X j  X j )]  

 C
 Xi X j
i j
i j
  X2 i


C
 Xi X j
i j
i j
C X 
1 1/2
f X1 ,
XN
 x1
x
x 2


 xN






(x1 ,
xN ) =
 X1 
 
X2 

X
 
 
XN 
(2 ) N /2
  x  X  t C 1  x  X  

 X 

exp  

2


 x1 - X 1 


x
X
2 
 x  X    2




x
X
N
 N
 C11 C12
C
C22
21

C X   

C N 1 C N 2
N  1 (one random variable)   x  X    x  X 
 f X (x) =
 
2
X
1 1/2
(2 )1/2
  x  X  t  2  1  x  X  

  X 
 =
exp  

2


C1N 
C2 N 



CNN 
C X   [ E[( X i  X i )2 ]   X2
2




x

X
1
 


exp 

2
2
2

2 X
X




N  2 (Two random variables) we have the joint density function
Example
Solution
 P[ X * (3)  1]  F (1)
 1  0.8413  0.1587
Example
Solution
 P[ X * (3)  1]  F (1)  0.8413
Linear System with Random Input
In application of random process, the Input-Output relation through a linear system
can be described as follows:
X(t)
Y(t)=X(t)*h(t)
h(t)
Linear System
Here X(t) is a random process and h(t) (Deterministic Function) is the impulse
response of the linear system ( Filter or any other Linear System )
Linear System with Random Input
X(t)
Y(t)=X(t)*h(t)
h(t)
Linear System
Now we can look at input output relation as follows:
The Time Domain
The output in the time domain is the convolution of the Input random process X(t)
and the impulse response h(t),




Y(t)=  X(ξ)h(t  ξ)dξ=  h(ξ)X(t  ξ)dξ
Question: Can you evaluate this convolution integral ?
Answer:
We can observe that we can not evaluate this convolution integral in general because
X(t) is random and there is no mathematical expression for X(t).
The Frequency Domain
The output in the Frequency Domain is the Product of the Input Fourier Transform
of the input random process X(t) , FX(f) and the Fourier Transform of the impulse
response h(t), H(f)
FX (f)
FY (f) = FX (f)H(f)
H(f)
Linear System

FX (f)=  X(t) e j2πf dt

the Fourier Transform of the input random process X(t)
is a random process

H(f)=  h(t) e j2πf dt
the Fourier Transform of the deterministic impulse response
FY (f) = FY (f)H(f)
the Fourier Transform of the output random process Y(t)
is a random process

Question : Can you evaluate the Fourier Transform of the input random process X(t) , FX(f) ?
Answer: In general no , since the function X(t) in general is random and has no mathematical
expression.
X(t)
Y(t)=X(t)*h(t)
h(t)
Linear System
Question: How can we describe then the behavior of the input random process and
the output random process through a linear time-invariant system ?
We defined previously the autocorrelation R X (τ) as
R X (τ)=E[X(t)X(t + τ)]
The auto correlation tell us how the random process is varying
Is it a slow varying process or a high varying process.
Next we will define another function that will help us on looking at the behavior of the
random process
Let
Sxx(f) ( or Sxx(w)) be the Fourier Transform of Rxx(t)
 R XX (τ)  SXX (f)
SXX (f) =

R XX (τ) =
Since



R XX (0) =
R XX (τ)e



SXX (f)e


 j2πfτ
j2πfτ
dτ
df
OR
OR
SXX (ω) =



R XX (τ)e jωτ dτ
1 
jωτ
R XX (τ) =
S
(ω)e
dω
XX

2π 
2
SXX (f)df = E[X(t)X(t)]=E[X (t)]
Average Power
Then SXX(f) is Power Spectral Density ( PSD ) of the Random Process X(t)
R XX (τ) =



SXX (f)e
j2πfτ
df 
autocorrelation
SXX (f)
=



R XX (τ)e  j2πfτdτ
Power Spectral Density
( PSD )
Properties of PSD
(1)
SXX (f )  0 for all f ( Power never negative)
(2)
SXX (f) = SXX (f)
(3)
SXX (f) is real since
(4)
(5)
R XX (0) =
SXX (0) =






Even Function ( X(t) real  R XX (τ) is real)
R XX (τ)
SXX (f)df
R XX (τ)dτ
is even function
E[X 2 (t)]
Total Power
DC power ( power at zero Frequency)
Now let us look at the input-output linear system shown below in the time domain
and Frequency domain assuming the Random Process X(t) is WSS
Time Domain
Random Function
X(t)
R XX (τ)
Random Function
Y(t)=X(t)*h(t)
h(t)
Linear System
R YY (τ)
None random Deterministic Function
None random Deterministic Function
Y = XH(0)
X
The Mean of the output,
E  Y(t) = E   h(ξ)X(t  ξ) dξ  =
 





h(ξ) E  X(t  ξ) dξ
X

= X  h(ξ)dξ = XH(0) = Y

Constant
Random Function
Random Function
X(t)
h(t)
R XX (τ)
Linear System
None random Deterministic Function
Y(t)=X(t)*h(t)
R YY (τ)
None random Deterministic Function
the mean is constant and the
autocorrelationof the output
is a function of
Y = XH(0)
X
R XX (τ)
R YY (τ) = R XX (τ)  h(  τ)  h(τ)
The Autocorrelation of the output,
R YY (t, t + τ) = E  Y(t)Y(t + τ)



= E  h(ξ1 )X(t  ξ1 ) dξ1
 

Y(t)
=
=


  E X(t
 

 

 
Y(t) is WSS



h(ξ
)X(t
+
τ

ξ
)
dξ
2
2
 2


Y(t + τ)
 ξ1 )X(t + τ  ξ 2 )h(ξ1 )h(ξ 2 )dξ1dξ 2
R XX (τ + ξ1  ξ 2 )h(ξ1 )h(ξ 2 )dξ1dξ 2 = R XX (τ)  h(  τ)  h(τ)
Random Function
Random Function
X(t)
h(t)
R XX (τ)
Y(t)=X(t)*h(t)
Linear System
None random Deterministic Function
R YY (τ)
None random Deterministic Function
Y = XH(0)
X
R YY (τ) = R XX (τ)
R XX (τ)
 h(  τ)  h(τ)
SYY (f ) = S XX (f ) 
S XX (f )
H (f )  H (f ) = S XX (f ) H ( f )
*
H( f )
Total power of the Input
Total power of the Output

2
E[X (t )]=R XX (0) =


S XX ( f )df
2
E[Y 2 (t )] = RYY (0) =



SYY ( f )df =



2
S XX ( f ) H ( f ) df
2