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Unformatted text preview: UCSB Spring 2010 ECE 146B: Problem Set 4 Assigned: May 3 Due: May 7 (by noon, in course homework box) Reading: Classnotes, Chapter 7 Topics: Gaussian models; Hypothesis testing; Signal space concepts Midterm: In class, Monday May 10. Covers Problem Sets 1 through 4, and Chapters 6 and 7 (up to Section 7.3). Midterm review lecture: Wednesday May 5. Change in office hours this week: Office hours on Friday 911 am for midterm. Office hours Wednesday are canceled. Problem 1: Consider binary signaling in AWGN, with s 1 ( t ) = (1  t  ) I [ 1 , 1] ( t ) and s ( t ) = s 1 ( t ). The received signal is given by y ( t ) = s i ( t ) + n ( t ), i = 0 , 1 , , where the noise n has PSD 2 = N 2 = 0 . 1. For all of the error probabilities computed in this problem, specify in terms of the Q function with positive arguments and also give numerical values. (a) How would you implement the ML receiver using the received signal y ( t )? What is its conditional error probability given that s is sent?is sent?...
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This note was uploaded on 12/28/2011 for the course ECE 146b taught by Professor Staff during the Fall '08 term at UCSB.
 Fall '08
 Staff

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