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Material Type: Assignment; Professor: Srikant; Class: Random Processes; Subject: Electrical and Computer Engr; University: University of Illinois - Urbana-Champaign; Term: Spring 2010;
Typology: Assignments
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ECE 534: Random Processes Spring 2010
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
∞
−∞
X
(ω)
dω
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
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
(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
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
(c) E[|Xt − Yt|
2 ] = E[(Xt − Yt)(Xt − Yt)
∗ ] = RX (0) − RXY (0) − RY X (0) + RY (0). Since
X
(0) = 1 and R XY
∗
Y X
Y X
(0), it follows that
E[|Xt − Yt|
2 ] = 1 − 2 R Y X
Y
a
4
a
3
a
6
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
t
1
2
for all t. Thus μ X
= 0. For τ ≥ 0
X
(τ ) = P [X τ
0
τ
0
τ
0
τ
0
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