Communication, Networking, Signal and Image Processing (CS)
Question 1: Probability and Random Processes
August 2001
4. (35 Points)
Let $ \left\{ t_{k}\right\} $ be the set of Poisson points corresponding to a homogeneous Poisson process with parameters $ \lambda $ on the real line such that if $ \mathbf{N}\left(t_{1},t_{2}\right) $ is defined as the number of points in the interval $ \left[t_{1},t_{2}\right) $ , then $ P\left(\left\{ N\left(t_{1},t_{2}\right)=k\right\} \right)=\frac{\left[\lambda\left(t_{2}-t_{1}\right)\right]^{k}e^{-\lambda\left(t_{2}-t_{1}\right)}}{k!}\;,\qquad k=0,1,2,\cdots,\; t_{2}>t_{1}\geq0. Let \mathbf{X}\left(t\right)=\mathbf{N}\left(0,t\right) $ be the Poisson counting process for $ t>0 $ (note that $ \mathbf{X}\left(0\right)=0 $ ).
(a) Find the (first order) characteristic function of $ \mathbf{X}\left(t\right) $ .
(b) Find the mean and variance of $ \mathbf{X}\left(t\right) $ .
(c) Derive an expression for the autocorrelation function of $ \mathbf{X}\left(t\right) $ .
(d) Assuming that $ t_{2}>t_{1} $ , find an expression for $ P\left(\left\{ \mathbf{X}\left(t_{1}\right)=m\right\} \cap\left\{ \mathbf{X}\left(t_{2}\right)=n\right\} \right) $ , for all $ m=0,1,2,\cdots $ and $ n=0,1,2,\cdots $ .
Solution 1
(a)
$ \Phi_{\mathbf{X}}\left(\omega\right)=E\left[e^{i\omega\mathbf{X}}\right]=\sum_{k=0}^{\infty}e^{i\omega k}\frac{\left(\lambda t\right)^{k}e^{-\lambda t}}{k!}=e^{-\lambda t}\sum_{k=0}^{\infty}\frac{\left(\lambda te^{i\omega}\right)^{k}}{k!}=e^{-\lambda t}e^{\lambda te^{i\omega}}=e^{-\lambda t\left(1-e^{i\omega}\right)}. $
(b)
$ E\left[\mathbf{X}\left(t\right)\right]=\frac{d}{di\omega}\Phi_{\mathbf{X}}\left(\omega\right)\biggl|_{i\omega=0}=\frac{d}{di\omega}e^{-\lambda t}e^{\lambda te^{i\omega}}\biggl|_{i\omega=0}=e^{-\lambda t}\cdot\frac{d}{di\omega}e^{\lambda te^{i\omega}}\biggl|_{i\omega=0} $$ =e^{-\lambda t}\cdot e^{\lambda te^{i\omega}}\cdot\lambda te^{i\omega}\biggl|_{i\omega=0}=e^{-\lambda t}\cdot e^{\lambda t}\cdot\lambda t=\lambda t. $
$ E\left[\mathbf{X}^{2}\left(t\right)\right]=\frac{d}{d\left(i\omega\right)^{2}}\Phi_{\mathbf{X}}\left(\omega\right)\biggl|_{i\omega=0}=\frac{d}{di\omega}\lambda te^{-\lambda t}e^{\lambda te^{i\omega}}e^{i\omega}\biggl|_{i\omega=0} $$ =\lambda te^{-\lambda t}\cdot\frac{d}{di\omega}e^{\lambda te^{i\omega}}e^{i\omega}\biggl|_{i\omega=0} $$ =\lambda te^{-\lambda t}\left(e^{\lambda te^{i\omega}}\lambda te^{i\omega}e^{i\omega}+e^{\lambda te^{i\omega}}e^{i\omega}\right)\biggl|_{i\omega=0} $$ =\lambda te^{-\lambda t}\left(\lambda te^{\lambda te^{i\omega}}e^{2i\omega}+e^{\lambda te^{i\omega}}e^{i\omega}\right)\biggl|_{i\omega=0}=\lambda te^{-\lambda t}\left(\lambda te^{\lambda t}+e^{\lambda t}\right) $$ =\lambda t\left(\lambda t+1\right)=\left(\lambda t\right)^{2}+\lambda t. $
$ Var\left[\mathbf{X}\left(t\right)\right]=E\left[\mathbf{X}^{2}\left(t\right)\right]-\left(E\left[\mathbf{X}\left(t\right)\right]\right)^{2}=\left(\lambda t\right)^{2}+\lambda t-\left(\lambda t\right)^{2}=\lambda t. $
(c)
$ R_{\mathbf{XX}}\left(t_{1},t_{2}\right) $
(d)
$ P\left(\left\{ \mathbf{X}\left(t_{1}\right)=m\right\} \cap\left\{ \mathbf{X}\left(t_{2}\right)=n\right\} \right) $
