Master Course Description

No: EE 416

Title: Random Signals for Communications and Signal Processing

Credits: 4

UW Course Catalog Description

Coordinator: James A. Ritcey, Professor of Electrical Engineering

Goals: To learn the techniques of applied probability and statistical signal processing and apply to communications and signal processing.

Learning Objectives: At the end of this course, students will be able to:

  1. Calculate the probability of combinations of events using hand and computer analysis.
  2. Write computer (MATLAB) programs to compute many probability distributions.
  3. Solve for the distributions of random variable arising from certain functions of random variables.
  4. Model datasets arising in communications using common probabilistic models.
  5. Analyze the effect of randomness on communication signals.
  6. Model and analyze the linear systems using multivariate Gaussian distributions.
  7. Design statistical signal processing systems for communications applications.

Textbook: John A. Gubner, Probability and Random Processes for Electrical and Computer Engineering, Cambridge University Press, 2006.

Prerequisites by Topic:

  1. Linear Systems Theory in Discrete and Continuous Time
  2. Basic Signals in Discrete and Continuous Time
  3. Differential and Integral Calculus
  4. Principles of Engineering Statistics
  5. Principles of Probability
  6. Facility with MATLAB

Topics:

  1. Models of Probability (2 weeks)
  2. Single Random Variables (2 weeks)
  3. Two Random Variables (2 weeks)
  4. Multiple Random Variables (2 weeks)
  5. Applications in Communications and Signal Processing (2 weeks)

Course Structure: The class meets for two lectures a week, each consisting of 75-minute sessions. There is weekly homework due that includes small computer projects in MATLAB. One major MATLAB project assigned at the end of the course. EE416 assesses performance in various ways, but includes 2 midterm exams and 1 comprehensive final exam.

Computer Resources: The computer project can be done on any PC or workstation that contains MATLAB. However, we only support the EE Dept computers, and some personal PCs may be insufficient due to memory, speed, software, etc..

Laboratory Resources: Access to the computer resources are all that is necessary.

Grading: 20% Homework, 40%midterms, 25 % final exam, 15% final project.

Outcome Coverage (Notation: (L) - low significance; (M) - medium significance; (H) - high significance):

(a) (H) An ability to apply knowledge of math, science and engineering. The course is highly mathematical in its orientation. Students are asked to both identify and solve appropriate mathemtical models. Engineering judgement is developed through the use of modeling and approximate solution techniques. Some basic principles of physical science are occasionally needed to motivate the engineering origins of the problems under consideration.

(b) (H) An ability to design and conduct experiments, as well as to analyze and interpret data. Students are asked to determine the required sample sizes for statistically reliable estimates in experimental settings. Monte Carlo simulation techniques are used to solve difficult problems not amenable to analysis and as substitutes for physical experiments. This is absolutely required in the field of communications, due to the low error rate requirements.

(c) (M) An ability to design a system, component or process to meet desired needs. The final project challenges the students to design and simulate a statistical signal processing system for communications. The exact application may vary with the offering. Recent examples have included interference rejection, radar detection and estimation, prediction and smoothing, and demodulation of spread spectrum signals.

(e) (H)An ability to identify, formulate and solve engineering problems. The homework involves solving engineering problems identified by the assignments and exemplified by class discussion. The midterm and final projects challenge the students to identify the issues and formulate their individual solutions.

(f) (H) An understanding of professional and ethical responsibilities. Professional ethics is discussed, but not in any great detail.

(g) (M) An ability to communicate effectively. Students are required to writeup their final design project in a specified engineering format. The ability to communicate effectively in writing is a portion of the grade received on this project.

(h) (M) The broad education necessary to understand the impact of engineering solutions in a global and societal context. Statistical reasoning is an important component of quality judgement. Many systems , both social and physical, succeed and fail on an random basis. The detection of small changes amid noise, whether physically generated or due to imperfect social systems, is important in modern society. Examples include risk assessment for events that are very unlikely, but have extreme consequences. This courses helps to build that judgement.

(i) (M) A recognition of the need for, and an ability to engage in life-long learning. This use of probability and statistics throughout a career is discussed, along with ways to grow personal knowledge in this area.

(j) (M) A knowledge of contemporary issues. Statistics in the the news is discussed.

(k) (H) An ability to use the techniques, skills,and modern engineering tools necessary for engineering practice. Students use analysis and Matlab to solve homework problems and final project.

Prepared By: James A. Ritcey

Reviewed by: Radha Poovendran (Communication and Networking Group Chair)

Last revised: 21/21/12