Problem Set 10 Resolution - Random Processes | ECE 534, Assignments of Electrical and Electronics Engineering

Material Type: Assignment; Professor: Srikant; Class: Random Processes; Subject: Electrical and Computer Engr; University: University of Illinois - Urbana-Champaign; Term: Spring 2010;

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ECE 534: Random Pro cesses Spring 2010
Solutions to Problem Set 10
8.1 On filtering a WSS random process
(a) True. Since SY(ω) = |H(ω)|2SX(ω), it follows that SY(ω)SX(ω) for all ω. Integrat-
ing over all frequencies yields the result, because the power of Xis R
−∞ SX(ω)
2πand
similarly for Y.
(c) False. The output power is zero if (and only if) SX(ω)|H(ω)|20. This happens, for
example, if H(ω) = I{|ω|≤2π}and SX(ω) = I{4π≤|ω|≤6π}.
8.5 Slight smoothing
(a) RY X (τ) = hRX(τ) = R
−∞ h(τt)RX(t)dt. Setting τ= 0, and using the symmetry
of hand of RXyields
RY X (0) = hRX(0) = Z
−∞
h(0 t)RX(t)dt
=2
aZa/2
0
etdt
=2
a(1 ea/2) = 2
a11 + a
21
2a
22+1
3! a
23+· · ·
= 1 a
4+a2
24 +· · · = 1 a
4+o(a)
(b) RY(τ) = he
hRX(τ) = R
−∞(he
h)(τt)RX(t)dt. First we find that he
his the
triangle function over the interval [a, a] with height 1/a. Thus, taking τ= 0 and using
the symmetry of he
hand of RXyields
RY(0) = Z
−∞
(he
h)(0 t)RX(t)dt
=2
a2Za
0
(at)etdt
=2
a2(a1 + ea) = 2
a2a2
2a3
3! +a4
4! · · ·
= 1 a
3+a2
12 +· · · = 1 a
3+o(a)
(c) E[|XtYt|2] = E[(XtYt)(XtYt)] = RX(0) RXY (0) RY X(0) + RY(0). Since
RX(0) = 1 and RXY (0) = R
Y X (0) = RY X(0), it follows that
E[|XtYt|2] = 1 2RY X(0) + RY(0) = 1 2(1 a
4+o(a)) + 1 a
3+o(a) = a
6+o(a)
1
pf2

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ECE 534: Random Processes Spring 2010

Solutions to Problem Set 10

8.1 On filtering a WSS random process

(a) True. Since S Y

(ω) = |H(ω)|

2 S X

(ω), it follows that S Y

(ω) ≤ S X

(ω) for all ω. Integrat-

ing over all frequencies yields the result, because the power of X is

−∞

S

X

(ω)

2 π

and

similarly for Y.

(c) False. The output power is zero if (and only if) S X

(ω)|H(ω)|

2 ≡ 0. This happens, for

example, if H(ω) = I {|ω|≤ 2 π}

and SX (ω) = I { 4 π≤|ω|≤ 6 π}

8.5 Slight smoothing

(a) R Y X

(τ ) = h ∗ R X

(τ ) =

−∞

h(τ − t)R X

(t)dt. Setting τ = 0, and using the symmetry

of h and of R X

yields

R

Y X

(0) = h ∗ R X

−∞

h(0 − t)R X

(t)dt

a

a/ 2

0

e

−t

dt

a

(1 − e

−a/ 2 ) =

a

a

a

2

a

3

a

a

2

a

  • o(a)

(b) RY (τ ) = h ∗

h ∗ RX (τ ) =

−∞

(h ∗

h)(τ − t)RX (t)dt. First we find that h ∗

h is the

triangle function over the interval [−a, a] with height 1/a. Thus, taking τ = 0 and using

the symmetry of h ∗

h and of RX yields

R

Y

−∞

(h ∗

h)(0 − t)R X

(t)dt

a

2

a

0

(a − t)e

−t

dt

a

2

(a − 1 + e

−a ) =

a

2

a

2

a

3

a

4

a

a

2

a

  • o(a)

(c) E[|Xt − Yt|

2 ] = E[(Xt − Yt)(Xt − Yt)

∗ ] = RX (0) − RXY (0) − RY X (0) + RY (0). Since

R

X

(0) = 1 and R XY

(0) = R

Y X

(−0) = R

Y X

(0), it follows that

E[|Xt − Yt|

2 ] = 1 − 2 R Y X

(0) + R

Y

a

4

  • o(a)) + 1 −

a

3

  • o(a) =

a

6

  • o(a)

1

ECE 534 HW10 Spring 2010

8.7 A stationary two-state Markov process in continuous time

πQ = 0 implies π = (

1

2

1

2

) is the equilibrium distribution so P [X t

= 1] = P [X

t

= −1] =

1

2

for all t. Thus μ X

= 0. For τ ≥ 0

R

X

(τ ) = P [X τ

= 1, X

0

= 1] − P [X

τ

= − 1 , X

0

= 1] − P [X

τ

= 1, X

0

= −1] + P [X

τ

= − 1 , X

0

= −1]

1 + e

− 2 ατ

1 − e

− 2 ατ

1 − e

− 2 ατ

1 + e

− 2 ατ

= e

− 2 ατ

So in general, R X

(τ ) = e

− 2 α|τ |

. (This is the same as the autocorrelation function of a

Gaussian Markov process.) The corresponding power spectral density is given by: SX (ω) =

4 α

4 α

2 +ω

2

2