Communication, Networking, Signal and Image Processing (CS)
Question 1: Probability and Random Processes
August 2007
3. (25 Points)
Let $ \mathbf{X}\left(t\right) $ be a real Gaussian random process with mean function $ \mu\left(t\right) $ and autocovariance function $ C_{\mathbf{XX}}\left(t_{1},t_{2}\right) $ .
(a)
Write the expression for the $ n $ -th order characteristic function of $ \mathbf{X}\left(t\right) $ in terms of $ \mu\left(t\right) $ and $ C_{\mathbf{XX}}\left(t_{1},t_{2}\right) $ .
ref.
There are the note about the n-th order characteristic function of Gaussians random process . The only difference between the note and this problem is that this problem use the $ \mu\left(t\right) $ rather than $ \eta_{\mathbf{X}}\left(t\right)=E\left[\mathbf{X}\left(t\right)\right] $ .
Solution
$ \Phi_{\mathbf{X}\left(t_{1}\right)\cdots\mathbf{X}\left(t_{n}\right)}\left(\omega_{1},\cdots,\omega_{n}\right)=\exp\left\{ i\sum_{k=1}^{n}\mu_{\mathbf{X}}\left(t_{k}\right)\omega_{k}-\frac{1}{2}\sum_{j=1}^{n}\sum_{k=1}^{n}C_{\mathbf{XX}}\left(t_{j},t_{k}\right)\omega_{j}\omega_{k}\right\} $ .
(b)
Show that the probabilistic description of $ \mathbf{X}\left(t\right) $ is completely characterized by $ \mu\left(t\right) $ and autocovariance function $ C_{\mathbf{XX}}\left(t_{1},t_{2}\right) $ .
Solution
From (a), the characteristic function of $ \mathbf{X}\left(t\right) $ is specified completely in terms of $ \mu_{\mathbf{X}}\left(t\right) $ and $ C_{\mathbf{XX}}\left(t_{1},t_{2}\right) $ . Thus, probabilistic description of $ \mathbf{X}\left(t\right) $ is completely characterized by the characteristic function.
Note
$ f_{\mathbf{X}}\left(x\right)=\frac{1}{2\pi}\int_{-\infty}^{\infty}e^{-i\omega x}\Phi_{\mathbf{X}}\left(\omega\right)d\omega. $
(c)
Show that if $ \mathbf{X}\left(t\right) $ is wide-sense stationary then it is also strict-sense stationary.
Note
You can use the theorem and its proof for solving this problem.
