EEweb Home     ::     Graduate Courses     ::     Undergraduate Courses     ::     My Home

ABET Course Objectives and Outcomes Form

Course number and title: EE132A Introduction to Communication Systems
Credits: 4
Instructor(s)-in-charge: S. Diggavi (suhas@ee.ucla.edu)
  J. Villasenor (villa@ee.ucla.edu)
Course type: Lecture
Required or Elective: Required.
Course Schedule:
Lecture: 4 hrs/week. Meets twice weekly.
Dicussion: 1 hr/discussion section. Multiple discussion sections offered per quarter.
Outside Study: 9 hrs/week.
Office Hours: 2 hrs/week by instructor. 2 hrs/week by each teaching assistant.
 
Course Assessment:
Homework: 7 assignments.
Exams: 1 midterm and 1 final examination
Design: Matlab-based design project
 
Grading Policy: Typically 10% design, 15% homework, 30% midterm, 45% final.
Course Prerequisites: EE102, EE113, EE131A.
Catalog Description: Properties of signals and noise. Baseband pulse and digital signaling. Bandpass signaling techniques. Communication systems: digital transmission, frequency-division multiplexing and telephone systems, satellite communication systems. Performance of communication systems in presence of noise.  
Textbook and any related course material:
Communication Systems Engineering, Proakis, John G. & Salehi, Masoud, 2nd Edition, Prentice-Hall, Inc.
 
Course Website
Topics covered in the course and level of coverage:
Signals and systems 6 hrs.
Analog modulation without noise 6 hrs.
Random Processes 6 hrs.
Analog modulation with noise 6 hrs.
Maximum Likelihood Decoding, Matched filtering. 2 hrs.
Intersymbol Interference-free signaling, 2 hrs.
Orthonormal basis functions, signal space (with noise), union bounds 4 hrs.
Digital Modulation Examples 4 hrs.
Capacity sharing techniques: CDMA, FDM, TDMA 2 hrs.
Course objectives and their relation to the Program Educational Objectives:  
Contribution of the course to the Professional Component:
Engineering Topics: 0 %
General Education: 0 %
Mathematics & Basic Sciences: 0 %
Expected level of proficiency from students entering the course:
Mathematics: Strong
Physics: Some
Chemistry: Not Applicable
Technical writing: Average
Computer Programming: Average
Material available to students and department at end of course:
  Available to
students
Available to
department
Available to
instructor
Available to
TA(s)
Course Objectives and Outcomes Form: X X X X
Lecture notes, homework assignments, and solutions: X
Samples of homework solutions from 2 students: X
Samples of exam solutions from 2 students: X
Course performance form from student surveys: X X
Will this course involve computer assignments? YES Will this course have TA(s) when it is offered? YES

  Level of contribution of course to Program Outcomes
(a) Strong  
(b) Some  
(c) Strong  
(g) Some  
(i) Strong  
(k) Average  
(l) Strong  
(m) Average  
(n) Average  
Strong: (a) (c) (i) (l)
Average: (k) (m) (n)
Some: (b) (g)

:: Upon completion of this course, students will have had an opportunity to learn about the following ::
  Specific Course Outcomes Program Outcomes
1. How to convert between time and frequency domain representations of signals. a m n
2. How to compute the energy in an energy signal in the time or frequency domain. a k m n
3. How to compute a modulated analog signal from an analog message signal (modulation). a k m n
4. How to compute an analog message signal from an analog modulated signal (demodulation). l
5. How to compute the autocorrelation function of a random process. l
6. How to determine whether a random process is stationary (if possible) or wide-sense stationary (WSS). l
7. How to determine if a random process is ergodic. l
8. How to determine the power spectral density (PSD) of WSS random processes. l
9. Determine the power in a power signal in the time or frequency domain. a k m n
10. How to compute the receiver signal-to-noise ratio (SNR) of analog modulations. a k m n
11. An understanding of the tradeoff in analog modulations between bandwidth, receiver SNR, and receiver complexity. k
12. How to compute the maximum likelihood choice among two or more hypotheses. k l
13. How to design a matched filter to facilitate maximum likelihood detection of an analog pulse. c
14. How to compute the PSD of a digital modulation. a k m n
15. How to design inter-symbol interference-free pulse shapes under bandwidth constraints. c
16. How to find the orthonormal basis functions of a digital modulation system. a k m n
17. How to analyze a digital modulation system with noise in signal space, computing union bounds on error probability. a k m n
18. Opportunity to conduct a Matlab-based design project requiring some independent reading, programming, simulations, and technical writing b c g
19. How to use library resources to find an answer in the scientific literature. i

  Program outcomes and how they are covered by the specific course outcomes
(a)   How to convert between time and frequency domain representations of signals.  
  How to compute the energy in an energy signal in the time or frequency domain.  
  How to compute a modulated analog signal from an analog message signal (modulation).  
  Determine the power in a power signal in the time or frequency domain.  
  How to compute the receiver signal-to-noise ratio (SNR) of analog modulations.  
  How to compute the PSD of a digital modulation.  
  How to find the orthonormal basis functions of a digital modulation system.  
  How to analyze a digital modulation system with noise in signal space, computing union bounds on error probability.  
(b)   Opportunity to conduct a Matlab-based design project requiring some independent reading, programming, simulations, and technical writing  
(c)   How to design a matched filter to facilitate maximum likelihood detection of an analog pulse.  
  How to design inter-symbol interference-free pulse shapes under bandwidth constraints.  
  Opportunity to conduct a Matlab-based design project requiring some independent reading, programming, simulations, and technical writing  
(g)   Opportunity to conduct a Matlab-based design project requiring some independent reading, programming, simulations, and technical writing  
(i)   How to use library resources to find an answer in the scientific literature.  
(k)   How to compute the energy in an energy signal in the time or frequency domain.  
  How to compute a modulated analog signal from an analog message signal (modulation).  
  Determine the power in a power signal in the time or frequency domain.  
  How to compute the receiver signal-to-noise ratio (SNR) of analog modulations.  
  An understanding of the tradeoff in analog modulations between bandwidth, receiver SNR, and receiver complexity.  
  How to compute the maximum likelihood choice among two or more hypotheses.  
  How to compute the PSD of a digital modulation.  
  How to find the orthonormal basis functions of a digital modulation system.  
  How to analyze a digital modulation system with noise in signal space, computing union bounds on error probability.  
(l)   How to compute an analog message signal from an analog modulated signal (demodulation).  
  How to compute the autocorrelation function of a random process.  
  How to determine whether a random process is stationary (if possible) or wide-sense stationary (WSS).  
  How to determine if a random process is ergodic.  
  How to determine the power spectral density (PSD) of WSS random processes.  
  How to compute the maximum likelihood choice among two or more hypotheses.  
(m)   How to convert between time and frequency domain representations of signals.  
  How to compute the energy in an energy signal in the time or frequency domain.  
  How to compute a modulated analog signal from an analog message signal (modulation).  
  Determine the power in a power signal in the time or frequency domain.  
  How to compute the receiver signal-to-noise ratio (SNR) of analog modulations.  
  How to compute the PSD of a digital modulation.  
  How to find the orthonormal basis functions of a digital modulation system.  
  How to analyze a digital modulation system with noise in signal space, computing union bounds on error probability.  
(n)   How to convert between time and frequency domain representations of signals.  
  How to compute the energy in an energy signal in the time or frequency domain.  
  How to compute a modulated analog signal from an analog message signal (modulation).  
  Determine the power in a power signal in the time or frequency domain.  
  How to compute the receiver signal-to-noise ratio (SNR) of analog modulations.  
  How to compute the PSD of a digital modulation.  
  How to find the orthonormal basis functions of a digital modulation system.  
  How to analyze a digital modulation system with noise in signal space, computing union bounds on error probability.  

:: Last modified: February 2013 by J. Lin ::

Copyright © 2003 UCLA Electrical and Computer Engineering Department. All rights reserved.
Please contact eeweb@ee.ucla.edu for comments or questions for the website.