1. EECS0712 Adaptive Signal Processing
1
Introduction to Adaptive Signal
Processing
EECS0712 Adaptive Signal Processing
1
Introduction to Adaptive Signal
Processing
Assoc. Prof. Dr. Peerapol Yuvapoositanon
Dept. of Electronic Engineering
CESdSP ASP1-1
EECS0712 Adaptive Signal Processing
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Assoc. Prof. Dr. P.Yuvapoositanon
2. Course Outline
• Introduction to Adaptive Signal Processing
• Adaptive Algorithms Families:
• Newton’s Method and Steepest Descent
• Least Mean Squared (LMS)
• Recursive Least Squares (RLS)
• Kalman Filtering
• Applications of Adaptive Signal Processing in
Communications and Blind Equalization
• Introduction to Adaptive Signal Processing
• Adaptive Algorithms Families:
• Newton’s Method and Steepest Descent
• Least Mean Squared (LMS)
• Recursive Least Squares (RLS)
• Kalman Filtering
• Applications of Adaptive Signal Processing in
Communications and Blind Equalization
CESdSP ASP1-2
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3. Evaluation
• Assignment= 20 %
• Midterm = 30 %
• Final = 50 %
CESdSP
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Assoc. Prof. Dr. P.Yuvapoositanon
ASP1-3
6. QR code
CESdSP
EECS0712 Adaptive Signal Processing
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Assoc. Prof. Dr. P.Yuvapoositanon
ASP1-6
7. Adaptive Signal Processing
• Definition: Adaptive signal processing is the
design of adaptive systems for signal-
processing applications.
[http://encyclopedia2.thefreedictionary.com/adaptive+signal+pr
ocessing]
• Definition: Adaptive signal processing is the
design of adaptive systems for signal-
processing applications.
[http://encyclopedia2.thefreedictionary.com/adaptive+signal+pr
ocessing]
CESdSP
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Assoc. Prof. Dr. P.Yuvapoositanon
ASP1-7
8. System Identification
• Let’s consider a system called “plant”
• We need to know its characteristics, i.e., The
impulse response of the system
CESdSP ASP1-8
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10. Error of Plant Outputs
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11. Error of Estimation
• Error of estimation is represented by the
signal energy of error
2 2
2 2
( )
2
e d y
d dy y
CESdSP ASP1-11
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Assoc. Prof. Dr. P.Yuvapoositanon
2 2
2 2
( )
2
e d y
d dy y
12. Adaptive System
• We can do it adaptively
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13. • Adjust the weight for minimum error e
One-weight
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14. 2 2
2 2
2 2
0 0 0 0
( )
2
( ) 2( )( ) ( )I I
e d y
d dy y
w x w x w x w x
CESdSP
2 2
2 2
2 2
0 0 0 0
( )
2
( ) 2( )( ) ( )I I
e d y
d dy y
w x w x w x w x
ASP1-14
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15. Error Curve
• Parabola equation
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16. Partial diff. and set to zero
• Partial differentiation
• Set to zero
• Result:
2
2 2
0 0 0 0
0 0
2 2
0 0
( ) 2( )( ) ( )
2 2
I I
I I
I
e
w x w x w x w x
w w
w x w x
• Partial differentiation
• Set to zero
• Result:
CESdSP
2
2 2
0 0 0 0
0 0
2 2
0 0
( ) 2( )( ) ( )
2 2
I I
I I
I
e
w x w x w x w x
w w
w x w x
2 2
0 00 2 2 I
w x w x
0 0
I
w w
ASP1-16
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Assoc. Prof. Dr. P.Yuvapoositanon
17. Multiple Weight Plants
• We calculate the weight adaptively
• Questions:
– What is the type of signal “x” to be used, e.g.
Sine, Cosine or Random signals ?
– If there is more than one weight w0 , i.e., w0….wN-
1, how do we calculate the solution?
• We calculate the weight adaptively
• Questions:
– What is the type of signal “x” to be used, e.g.
Sine, Cosine or Random signals ?
– If there is more than one weight w0 , i.e., w0….wN-
1, how do we calculate the solution?
CESdSP ASP1-17
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18. Plants with Multiple Weight
• If we have multiple weights
CESdSP
1
0 1w w z
w
ASP1-18
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Assoc. Prof. Dr. P.Yuvapoositanon
19. • In the case of two-weight
Two-weight
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20. Input
• From
• We construct the x as vector with first
element is the most recent
(3), (2), (1), (0), ( 1), ( 2),...x x x x x x
• From
• We construct the x as vector with first
element is the most recent
CESdSP
[ (3) (2) (1) (0)...]T
x x x xx
ASP1-20
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Assoc. Prof. Dr. P.Yuvapoositanon
21. Plants with Multiple Weight
(aka “Transversal Filter”)
• If we have multiple weights
( )x n ( 1)x n
CESdSP
0 ( )w x n
0 ( 1)w x n
0 0( ) ( ) ( 1)y n w x n w x n
ASP1-21
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22. Regression input signal vector
• If the current time is n, we have “Regression
input signal vector”
[ ( ) ( 1) ( 2) ( 3)...]T
x n x n x n x n x
CESdSP
[ ( ) ( 1) ( 2) ( 3)...]T
x n x n x n x n x
ASP1-22
EECS0712 Adaptive Signal Processing
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Assoc. Prof. Dr. P.Yuvapoositanon
23. 0
0 1
1
[ ]T
w
w ww
w
CESdSP
0
0 1
1
[ ]T
w
w ww
w
0
0 1
1
ˆ [ ]
I
I I T
I
w
w w
w
w
ASP1-23
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Assoc. Prof. Dr. P.Yuvapoositanon
24. Convolution
• Output of plant is a convolution
• Ex For N=2
1
1
( ) ( )
N
k
k
y n w x n k
• Output of plant is a convolution
• Ex For N=2
CESdSP
1
1
( ) ( )
N
k
k
y n w x n k
0 0( ) ( 0) ( 1)y n w x n w x n
ASP1-24
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Assoc. Prof. Dr. P.Yuvapoositanon
25. 0 1
0 1
0 1
0 1
0 1
(3) (3) (2)
(2) (2) (1)
(1) (1) (0)
(0) (0) ( 1)
( 1) ( 1) ( 2)
y w x w x
y w x w x
y w x w x
y w x w x
y w x w x
CESdSP
0 1
0 1
0 1
0 1
0 1
(3) (3) (2)
(2) (2) (1)
(1) (1) (0)
(0) (0) ( 1)
( 1) ( 1) ( 2)
y w x w x
y w x w x
y w x w x
y w x w x
y w x w x
ASP1-25
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Assoc. Prof. Dr. P.Yuvapoositanon
26. • We can use a vector-matrix multiplication
• For example, for n=3 we construct y(3) as
• For example, for n=1 we construct y(1) as
0 1 0 1
(3)
(3) (3) (2) [ ] (3)
(2)
T
x
y w x w x w w
x
w x
• We can use a vector-matrix multiplication
• For example, for n=3 we construct y(3) as
• For example, for n=1 we construct y(1) as
CESdSP
0 1 0 1
(3)
(3) (3) (2) [ ] (3)
(2)
T
x
y w x w x w w
x
w x
0 1 0 1
(1)
(1) (1) (0) [ ] (1)
(0)
T
x
y w x w x w w
x
w x
ASP1-26
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Assoc. Prof. Dr. P.Yuvapoositanon
27. 0 1 0 1
0 1 0 1
0 1 0 1
0 1 0 1
(3)
(3) (3) (2) [ ] (3)
(2)
(2)
(2) (2) (1) [ ] (2)
(1)
(1)
(1) (1) (0) [ ] (1)
(0)
(2)
(0) (0) ( 1) [ ] (0
(1)
T
T
T
T
x
y w x w x w w
x
x
y w x w x w w
x
x
y w x w x w w
x
x
y w x w x w w
x
w x
w x
w x
w x )
CESdSP
0 1 0 1
0 1 0 1
0 1 0 1
0 1 0 1
(3)
(3) (3) (2) [ ] (3)
(2)
(2)
(2) (2) (1) [ ] (2)
(1)
(1)
(1) (1) (0) [ ] (1)
(0)
(2)
(0) (0) ( 1) [ ] (0
(1)
T
T
T
T
x
y w x w x w w
x
x
y w x w x w w
x
x
y w x w x w w
x
x
y w x w x w w
x
w x
w x
w x
w x )
ASP1-27
EECS0712 Adaptive Signal Processing
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Assoc. Prof. Dr. P.Yuvapoositanon
28. • The error squared is
• Let us stop there to consider Random signal
theory first.
2 2
2 2
2 2
( )
2
ˆ ˆ( ) 2( )( ) ( )T T T T
e d y
d dy y
w x w x w x w x
• The error squared is
• Let us stop there to consider Random signal
theory first.
CESdSP
2 2
2 2
2 2
( )
2
ˆ ˆ( ) 2( )( ) ( )T T T T
e d y
d dy y
w x w x w x w x
ASP1-28
EECS0712 Adaptive Signal Processing
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Assoc. Prof. Dr. P.Yuvapoositanon
29. Review of Random Signals
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30. Wireless Transmissions
• Ideal signal transmission
11 00 11 00 11 0011 11 11 000011
CESdSP
EECS0712 Adaptive Signal Processing
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Assoc. Prof. Dr. P.Yuvapoositanon
ASP2-30
11 00 11 00 11 0011 11 11 000011
Information
Information is Random
32. Random Variable
• Random variable is a function
• For a single time Coin Tossing
1,
( )
-1,
x H
X x
x T
• Random variable is a function
• For a single time Coin Tossing
CESdSP
1,
( )
-1,
x H
X x
x T
ASP1-32
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Assoc. Prof. Dr. P.Yuvapoositanon
33. Our signal x(n) is a Random
Variable
• For a series of Coin Tossing
1,
( )
-1,
i
i
i
x H
X x
x T
• For a series of Coin Tossing
CESdSP
1,
( )
-1,
i
i
i
x H
X x
x T
0 1 2 3 4{ , , , , ,....}x x x x x x
ASP1-33
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Assoc. Prof. Dr. P.Yuvapoositanon
34. Coin tossing and Random Variable
• If random
• We have random variable X
0 1 2 3 4
{ , , , , }
{ , , , , }
x H H T H T
x x x x x
CESdSP
• If random
• We have random variable X
0 1 2 3 4( ) { ( ), ( ), ( ), ( ), ( )}
{ ( ), ( ), ( ), ( ), ( )}
{1,1, 1,1, 1}
iX x X x X x X x X x X x
X H X H X T X H X T
ASP1-34
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Assoc. Prof. Dr. P.Yuvapoositanon
35. Random Digital Signal
• If the random variable is a function of time, it
is called a stochastic process
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36. Probability Mass Function
• We need also to define the probability of each
random variable
( ) { ( ), ( ), ( ), ( ), ( )}
{1,1, 1,1, 1}
X x X H X H X T X H X T
CESdSP
( ) { ( ), ( ), ( ), ( ), ( )}
{1,1, 1,1, 1}
X x X H X H X T X H X T
ASP1-36
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37. Probability Mass Function
• PMF is for Discrete distribution function
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38. Time and Emsemble
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39. Probability of X(2)
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40. Probability Density Function
• PDF is for Continuous Distribution Function
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42. Probability Density Function
• PDF values can be > 1 as long as its area under
curve is 1
2
CESdSP
1/2
2
1
1
ASP1-42
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43. Cumulative Distribution Function
CESdSP
( ( )) Pr[ ( )]P x n X x n x
ASP1-43
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44. ( )
( ( )) ( )
x n
P x n p z dz
x x
CESdSP
( )
( ( )) ( )
x n
P x n p z dz
x x
ASP1-44
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45. Expectation Operator
{}E
CESdSP
{}E
ASP1-45
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46. Expected Value
• Expected value is known as the “Mean”
{ } ( )X XE x xp x dx
CESdSP
{ } ( )X XE x xp x dx
ASP1-46
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Assoc. Prof. Dr. P.Yuvapoositanon
47. Example of Expected Value
(Discrete)
• We toss a die N times and get a set of
outcomes
• Suppose we roll a die with N=6, we might get
{ ( )} { (1), (2), (3),..., ( )}X i X X X X N
• We toss a die N times and get a set of
outcomes
• Suppose we roll a die with N=6, we might get
CESdSP
{ ( )} { (1), (2), (3),..., ( )}X i X X X X N
{ ( )} {2,3,6,3,1,1}X i
ASP1-47
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Assoc. Prof. Dr. P.Yuvapoositanon
48. Example of Expected Value
(Discrete)
• But, empirically we have Empirical (Monte
Carlo) estimate as Expected Value
6
1
{ } ( )Pr( ( ))
1 1 1 1
1 2 3 6
3 6 3 6
2.67
X
i
E x X i X X i
CESdSP
6
1
{ } ( )Pr( ( ))
1 1 1 1
1 2 3 6
3 6 3 6
2.67
X
i
E x X i X X i
ASP1-48
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Assoc. Prof. Dr. P.Yuvapoositanon
49. Theoretical Expected Value
• But in theory, for a die
6
1
{ } ( )Pr( ( ))
1 1 1 1 1 1
1 2 3 4 5 6
6 6 6 6 6 6
3.5
X
i
E X X i X X i
1
Pr( ( ))
6
X X i
CESdSP
6
1
{ } ( )Pr( ( ))
1 1 1 1 1 1
1 2 3 4 5 6
6 6 6 6 6 6
3.5
X
i
E X X i X X i
ASP1-49
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50. Ensemble Average
i ensembles
1 1 2 2Ensemble Average of (1) (1)Pr[ (1)] (1)Pr[ (1)]
(1)Pr[ (1)]N N
x x x x x
x x
1 ensemble
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ASP1-50
i ensembles
51. Ensemble Average
{ ( )}E x n
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ASP1-51
{ ( )} ( ) ( ( )) ( )E x n x n p x n dx n
x
{ ( )}E x n
52. • I) Linearity
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ASP1-52
{ ( ) ( )} { ( )} { ( )}E ax n by n aE x n bE y n
53. • II)
{ ( ) ( )} { ( )} { ( )}E x n y n E x n E y n
CESdSP
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ASP1-53
{ ( ) ( )} { ( )} { ( )}E x n y n E x n E y n
54. • III)
{ ( )} ( ( )) ( ( )) ( )E y n g x n p x n dx n
x
CESdSP
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ASP1-54
{ ( )} ( ( )) ( ( )) ( )E y n g x n p x n dx n
x
55. Autocorrelation
1 1( , ) { ( ) ( )}r n m E x n x mxx
CESdSP
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ASP1-55
1 11 1 1 1 1 1( , ) ( ) ( ) ( ( ), ( )) ( ) ( )r n m x n x m p x n x m dx n x m
xx x x
56. 1 1(1,4) { (1) (4)}r E x xxx
CESdSP
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ASP1-56
57. Autocorrelation
• n=m
2
( , ) ( , ) { ( )}r n m r n n E x n xx xx
CESdSP
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ASP1-57
2
( , ) ( , ) { ( )}r n m r n n E x n xx xx
58. Autocorrelation Matrix
(0,0) (0,1) (0, 1)
(1,0) (1,1) (1, 1)
( 1,0) ( 1,1) ( 1, 1)
r r r N
r r r N
r N r N r N N
xx xx xx
xx xx xx
xx
xx xx xx
R
CESdSP
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ASP1-58
(0,0) (0,1) (0, 1)
(1,0) (1,1) (1, 1)
( 1,0) ( 1,1) ( 1, 1)
r r r N
r r r N
r N r N r N N
xx xx xx
xx xx xx
xx
xx xx xx
R
59. Covariance
( , ) {[ ( ) ( )][ ( ) ( )]}c n m E x n n x m m xx
CESdSP
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ASP1-59
( , ) {[ ( ) ( )][ ( ) ( )]}c n m E x n n x m m xx
60. Stationarity (I)
• I)
{ ( )} { ( )}E x n E x m
n1
CESdSP
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Assoc. Prof. Dr. P.Yuvapoositanon
ASP1-60
n2
61. Stationarity (II)
• II)
( , ) { ( ) ( )}r n n m E x n x n m xx
1 1 1 1( , ) { ( ) ( )}r n n m E x n x n m xx
CESdSP
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Assoc. Prof. Dr. P.Yuvapoositanon
ASP1-61
1 1 1 1( , ) { ( ) ( )}r n n m E x n x n m xx
62. Expected Value of Error Energy
• Let’s take the expected value of error energy
2 2 2
ˆ ˆ{ } {( ) 2( )( ) ( ) }
ˆ ˆ ˆ{( )( )} 2 {( )( )} {( )( )}
ˆ ˆ ˆ{ } 2 {( )( )} { }
ˆ ˆ ˆ2 {( )( )}
T T T T
T T T T T T
T T T T T T
T T T T
E e E
E E E
E E E
E
w x w x w x w x
w x x w x w w x w x x w
w xx w x w x w w xx w
w Rw x w x w w Rw
CESdSP
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Assoc. Prof. Dr. P.Yuvapoositanon
ASP1-62
2 2 2
ˆ ˆ{ } {( ) 2( )( ) ( ) }
ˆ ˆ ˆ{( )( )} 2 {( )( )} {( )( )}
ˆ ˆ ˆ{ } 2 {( )( )} { }
ˆ ˆ ˆ2 {( )( )}
T T T T
T T T T T T
T T T T T T
T T T T
E e E
E E E
E E E
E
w x w x w x w x
w x x w x w w x w x x w
w xx w x w x w w xx w
w Rw x w x w w Rw
63. Vector-Matrix Differentiation
ˆI)
ˆ
ˆ ˆ ˆII) 2
ˆ
T
T T
w x x
w
w xx w Rw
w
CESdSP
ˆI)
ˆ
ˆ ˆ ˆII) 2
ˆ
T
T T
w x x
w
w xx w Rw
w
ASP1-63
EECS0712 Adaptive Signal Processing
http://embedsigproc.wordpress.com/eecs0712
Assoc. Prof. Dr. P.Yuvapoositanon
64. Partial diff. and set to zero
• Differentiation
• Result:
ˆ0 2 {( ) } 2
ˆ
ˆ2 { } 2
ˆ2 2
T
E
E d
w x x Rw
w
x Rw
r Rw
• Differentiation
• Result:
CESdSP
ˆ0 2 {( ) } 2
ˆ
ˆ2 { } 2
ˆ2 2
T
E
E d
w x x Rw
w
x Rw
r Rw
1
ˆ
w R r
ASP1-64
EECS0712 Adaptive Signal Processing
http://embedsigproc.wordpress.com/eecs0712
Assoc. Prof. Dr. P.Yuvapoositanon
65. 2-D Error surface
CESdSP
1
ˆ
w R r
ASP1-65
EECS0712 Adaptive Signal Processing
http://embedsigproc.wordpress.com/eecs0712
Assoc. Prof. Dr. P.Yuvapoositanon
66. Four Basic Classes of Adaptive
Signal Processing
• I) Identification
• II) Inverse Modelling
• III) Prediction
• IV) Interference Cancelling
CESdSP
EECS0712 Adaptive Signal Processing
http://embedsigproc.wordpress.com/eecs0712
Assoc. Prof. Dr. P.Yuvapoositanon
ASP1-66
• I) Identification
• II) Inverse Modelling
• III) Prediction
• IV) Interference Cancelling
67. The Four Classes of Adaptive
Filtering
CESdSP
EECS0712 Adaptive Signal Processing
http://embedsigproc.wordpress.com/eecs0712
Assoc. Prof. Dr. P.Yuvapoositanon
ASP1-67