Problem 4: Let X(t) be a random process as: X(t) = cos(2лft+), where is a random variable distributed uniformly over (0, 2). This means that its PDF is 1 for Є[0,2] fo()=2π 0 otherwise (a) Determine the expectation of X(t), mx(t). (b) Determine the autocorrelation function of X(t), Rx(t,t+t). (c) Is X(t) a wide sense stationary (WSS) process? Explain why. (d) Determine the power spectral density (PSD) of X(t), Sx(f).
Problem 4: Let X(t) be a random process as: X(t) = cos(2лft+), where is a random variable distributed uniformly over (0, 2). This means that its PDF is 1 for Є[0,2] fo()=2π 0 otherwise (a) Determine the expectation of X(t), mx(t). (b) Determine the autocorrelation function of X(t), Rx(t,t+t). (c) Is X(t) a wide sense stationary (WSS) process? Explain why. (d) Determine the power spectral density (PSD) of X(t), Sx(f).
Introductory Circuit Analysis (13th Edition)
13th Edition
ISBN:9780133923605
Author:Robert L. Boylestad
Publisher:Robert L. Boylestad
Chapter1: Introduction
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![Problem 4:
Let X(t) be a random process as: X(t) = cos(2лft+),
where is a random variable distributed uniformly over (0, 2). This means that its PDF is
1
for Є[0,2]
fo()=2π
0
otherwise
(a) Determine the expectation of X(t), mx(t).
(b) Determine the autocorrelation function of X(t), Rx(t,t+t).
(c) Is X(t) a wide sense stationary (WSS) process? Explain why.
(d) Determine the power spectral density (PSD) of X(t), Sx(f).](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fc231fac6-f61a-45b2-b174-05bc9b27b74a%2Fc4e785c5-731c-4fd8-9f0d-0c7ad5539bb8%2Ft727p6w_processed.jpeg&w=3840&q=75)
Transcribed Image Text:Problem 4:
Let X(t) be a random process as: X(t) = cos(2лft+),
where is a random variable distributed uniformly over (0, 2). This means that its PDF is
1
for Є[0,2]
fo()=2π
0
otherwise
(a) Determine the expectation of X(t), mx(t).
(b) Determine the autocorrelation function of X(t), Rx(t,t+t).
(c) Is X(t) a wide sense stationary (WSS) process? Explain why.
(d) Determine the power spectral density (PSD) of X(t), Sx(f).
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