This preview shows pages 1–2. Sign up to view the full content.
This preview has intentionally blurred sections. Sign up to view the full version.
View Full Document
Unformatted text preview: ECE 5510: Random Processes Lecture Notes Fall 2008 Lecture 14 Today: (1) Decorrelation Transform (continued) (2) Joint Gaus sian R.V.s HW 6 is due today; HW 7 is due a week from today, at 5pm. Discussion item? 0.1 Linear Transforms of R.V.s Continued Example: Average and Difference of Two i.i.d. R.V.s We represent the arrival of two people for a meeting as r.v.s X 1 and X 2 . Let X = [ X 1 ,X 2 ] T . Assume that the two people arrive independently, with the same variance 2 and mean . Consider the average arrival time Y 1 = ( X 1 + X 2 ) / 2, and the difference between the arrival times, Y 2 = X 1 X 2 . The latter is a wait time that one person must wait before the second person arrives. Show that the average time and the difference between the times are uncorrelated. You can take these steps to solve this problem: 1. Let Y = [ Y 1 ,Y 2 ] T . What is the transform matrix A in the relation Y = A X ? 2. What is the mean matrix Y = E Y [ Y ]? (Note this isnt really needed to answer the question, but is good practice anyway.) 3. What is the covariance matrix of Y ? 4. How does the covariance matrix show that the two are uncor related? 1 Joint Gaussian r.v.s We often (OFTEN) see joint Gaussian r.v.s. E.g. ECE 5520, Digi tal Communications, joint Gaussian r.v.s are everywhere. In addi tion, the joint Gaussian R.V. is extremely important in statistics, economics, other areas of engineering. We cant overemphasize its importance. In many areas of the sciences, the Gaussian r.v.importance....
View
Full
Document
This note was uploaded on 09/15/2011 for the course ECE 5510 taught by Professor Chen,r during the Fall '08 term at University of Utah.
 Fall '08
 Chen,R

Click to edit the document details