(2 intermediate revisions by 2 users not shown) | |||
Line 6: | Line 6: | ||
[[Category:probability]] | [[Category:probability]] | ||
− | = [[ECE_PhD_Qualifying_Exams|ECE Ph.D. Qualifying Exam]] | + | <center> |
+ | <font size= 4> | ||
+ | [[ECE_PhD_Qualifying_Exams|ECE Ph.D. Qualifying Exam]] | ||
+ | </font size> | ||
+ | |||
+ | <font size= 4> | ||
+ | Communication, Networking, Signal and Image Processing (CS) | ||
+ | |||
+ | Question 1: Probability and Random Processes | ||
+ | </font size> | ||
+ | |||
+ | January 2006 | ||
+ | </center> | ||
+ | ---- | ||
---- | ---- | ||
==Question== | ==Question== | ||
− | + | 1 (33 points) | |
− | + | Let <math class="inline">\mathbf{X}</math> and <math class="inline">\mathbf{Y}</math> be two joinly distributed random variables having joint pdf | |
+ | |||
+ | <math class="inline">f_{\mathbf{XY}}\left(x,y\right)=\left\{ \begin{array}{lll} | ||
+ | 1, & & \text{ for }0\leq x\leq1\text{ and }0\leq y\leq1\\ | ||
+ | 0, & & \text{ elsewhere. } | ||
+ | \end{array}\right.</math> | ||
+ | |||
+ | (a) | ||
+ | |||
+ | Are <math class="inline">\mathbf{X}</math> and <math class="inline">\mathbf{Y}</math> statistically independent? Justify your answer. | ||
+ | |||
+ | |||
+ | (b) | ||
+ | |||
+ | Let <math class="inline">\mathbf{Z}</math> be a new random variable defined as <math class="inline">\mathbf{Z}=\mathbf{X}+\mathbf{Y}</math> . Find the cdf of <math class="inline">\mathbf{Z}</math> . | ||
+ | |||
+ | (c) | ||
+ | |||
+ | Find the variance of <math class="inline">\mathbf{Z}</math> . | ||
:'''Click [[ECE_PhD_QE_CNSIP_Jan_2006_Problem1.1|here]] to view student [[ECE_PhD_QE_CNSIP_Jan_2006_Problem1.1|answers and discussions]]''' | :'''Click [[ECE_PhD_QE_CNSIP_Jan_2006_Problem1.1|here]] to view student [[ECE_PhD_QE_CNSIP_Jan_2006_Problem1.1|answers and discussions]]''' | ||
---- | ---- | ||
− | + | 2 (33 points) | |
− | + | Suppose that <math class="inline">\mathbf{X}</math> and <math class="inline">\mathbf{N}</math> are two jointly distributed random variables, with <math class="inline">\mathbf{X}</math> being a continuous random variable that is uniformly distributed on the interval <math class="inline">\left(0,1\right)</math> and <math class="inline">\mathbf{N}</math> being a discrete random variable taking on values <math class="inline">0,1,2,\cdots</math> and having conditional probability mass function <math class="inline">p_{\mathbf{N}}\left(n|\left\{ \mathbf{X}=x\right\} \right)=x^{n}\left(1-x\right),\quad n=0,1,2,\cdots</math> . | |
+ | |||
+ | (a) | ||
+ | |||
+ | Find the probability that \mathbf{N}=n . | ||
+ | |||
+ | (b) | ||
+ | |||
+ | Find the conditional density of <math class="inline">\mathbf{X}</math> given <math class="inline">\left\{ \mathbf{N}=n\right\}</math> . | ||
+ | |||
+ | (c) | ||
+ | |||
+ | Find the minimum mean-square error estimator of <math class="inline">\mathbf{X}</math> given <math class="inline">\left\{ \mathbf{N}=n\right\}</math> . | ||
:'''Click [[ECE_PhD_QE_CNSIP_Jan_2006_Problem1.2|here]] to view student [[ECE_PhD_QE_CNSIP_Jan_2006_Problem1.2|answers and discussions]]''' | :'''Click [[ECE_PhD_QE_CNSIP_Jan_2006_Problem1.2|here]] to view student [[ECE_PhD_QE_CNSIP_Jan_2006_Problem1.2|answers and discussions]]''' | ||
---- | ---- | ||
− | + | 3 (34 points) | |
− | + | Assume that the locations of cellular telephone towers can be accurately modeled by a 2-dimensional homogeneous Poisson process for which the following two facts are know to be true: | |
− | + | 1. The number of towers in a region of area A is a Poisson random variable with mean \lambda A , where \lambda>0 . | |
− | + | ||
− | + | ||
− | + | 2. The number of towers in any two disjoint regions are statistically independent. | |
− | :'''Click [[ECE_PhD_QE_CNSIP_Jan_2006_Problem1. | + | Assume you are located at a point we will call the origin within this 2-dimensional region, and let <math class="inline">R_{\left(1\right)}<R_{\left(2\right)}<R_{\left(3\right)}<\cdots</math> be the ordered distances between the origin and the towers. |
+ | |||
+ | (a) Show that <math class="inline">R_{\left(1\right)}^{2},R_{\left(2\right)}^{2},R_{\left(3\right)}^{2},\cdots</math> are the points of a one-dimensional homogeneous Poisson process. | ||
+ | |||
+ | (b) What is the rate of the Poisson process in part (a)? <math class="inline">\lambda\pi</math> . | ||
+ | |||
+ | (c) Determine the density function of <math class="inline">R_{\left(k\right)}</math> , the distance to the <math class="inline">k</math> -th nearest cell tower. | ||
+ | |||
+ | :'''Click [[ECE_PhD_QE_CNSIP_Jan_2006_Problem1.3|here]] to view student [[ECE_PhD_QE_CNSIP_Jan_2006_Problem1.3|answers and discussions]]''' | ||
---- | ---- | ||
[[ECE_PhD_Qualifying_Exams|Back to ECE Qualifying Exams (QE) page]] | [[ECE_PhD_Qualifying_Exams|Back to ECE Qualifying Exams (QE) page]] |
Latest revision as of 10:24, 10 March 2015
Communication, Networking, Signal and Image Processing (CS)
Question 1: Probability and Random Processes
January 2006
Question
1 (33 points)
Let $ \mathbf{X} $ and $ \mathbf{Y} $ be two joinly distributed random variables having joint pdf
$ f_{\mathbf{XY}}\left(x,y\right)=\left\{ \begin{array}{lll} 1, & & \text{ for }0\leq x\leq1\text{ and }0\leq y\leq1\\ 0, & & \text{ elsewhere. } \end{array}\right. $
(a)
Are $ \mathbf{X} $ and $ \mathbf{Y} $ statistically independent? Justify your answer.
(b)
Let $ \mathbf{Z} $ be a new random variable defined as $ \mathbf{Z}=\mathbf{X}+\mathbf{Y} $ . Find the cdf of $ \mathbf{Z} $ .
(c)
Find the variance of $ \mathbf{Z} $ .
- Click here to view student answers and discussions
2 (33 points)
Suppose that $ \mathbf{X} $ and $ \mathbf{N} $ are two jointly distributed random variables, with $ \mathbf{X} $ being a continuous random variable that is uniformly distributed on the interval $ \left(0,1\right) $ and $ \mathbf{N} $ being a discrete random variable taking on values $ 0,1,2,\cdots $ and having conditional probability mass function $ p_{\mathbf{N}}\left(n|\left\{ \mathbf{X}=x\right\} \right)=x^{n}\left(1-x\right),\quad n=0,1,2,\cdots $ .
(a)
Find the probability that \mathbf{N}=n .
(b)
Find the conditional density of $ \mathbf{X} $ given $ \left\{ \mathbf{N}=n\right\} $ .
(c)
Find the minimum mean-square error estimator of $ \mathbf{X} $ given $ \left\{ \mathbf{N}=n\right\} $ .
- Click here to view student answers and discussions
3 (34 points)
Assume that the locations of cellular telephone towers can be accurately modeled by a 2-dimensional homogeneous Poisson process for which the following two facts are know to be true:
1. The number of towers in a region of area A is a Poisson random variable with mean \lambda A , where \lambda>0 .
2. The number of towers in any two disjoint regions are statistically independent.
Assume you are located at a point we will call the origin within this 2-dimensional region, and let $ R_{\left(1\right)}<R_{\left(2\right)}<R_{\left(3\right)}<\cdots $ be the ordered distances between the origin and the towers.
(a) Show that $ R_{\left(1\right)}^{2},R_{\left(2\right)}^{2},R_{\left(3\right)}^{2},\cdots $ are the points of a one-dimensional homogeneous Poisson process.
(b) What is the rate of the Poisson process in part (a)? $ \lambda\pi $ .
(c) Determine the density function of $ R_{\left(k\right)} $ , the distance to the $ k $ -th nearest cell tower.
- Click here to view student answers and discussions