COURSE TITLE: EECS 422 Random Processes in Communications and Control - I

CATALOG DESCRIPTION: Fundamentals of random variables; mean-squared estimation; limit theorems and convergence; definition of random processes; autocorrelation and stationarity; Gaussian and Poisson processes; Markov chains.

REQUIRED TEXTS: Alberto Leon-Garcia, Probability and Random Processes for Electrical Engineering, 3rd Ed.

REFERENCE TEXTS:

A. Papoulis and Pillai, Probability, Random Variables and Stochastic Processes, 4th Edition.

Stark & Woods, Probability, Random Processes, and Estimation for Engineers

Carl Helstrom, Probability and Stochastic Processes for Engineers

Sheldon Ross, Introduction to Probability Models

Sheldon Ross, Stochastic Processes

COURSE GOALS: To provide entering graduate students with a broad coverage of random processes that will serve as a foundation for advanced courses in their specializations, particularly in communications, networks, and signal processing.

PREREQUISITES BY COURSES: EECS 302 and EECS 222.

PREREQUISITES BY TOPIC:

1. Probability theory.

2. Frequency spectrum, Fourier transforms.

DETAILED COURSE TOPICS:

Note: Not every section of every chapter is covered in the course. Also, we may curtail topics at the end depending on comprehension.

Week 1: Review of probability theory. Ch. 1 and 2

Week 2: Review of random variables. Ch. 3 and 4

Week 3: Multiple random variables. Ch. 5 and 6

Week 4: Limit theorems and estimation. Ch. 7 and Ch. 8

Week 5: Introduction to random processes. Ch. 9.1 to 9.3

Week 6: Poisson and Wiener processes. Ch. 9.4 and 9.5

Week 7: Properties of random processes. Ch. 9.7 and 9.8

Week 8: Power spectrum and linear systems. Ch. 10

Week 9: Markov chains. Ch. 11

Week 10: Queueing models. Ch. 12

COURSE OBJECTIVES: When a student completes this course, s/he should be able to:

1. Understand the description and behavior of random processes.

2. Model basic classes of random processes.

COMPUTER USAGE: Optional.

LABORATORY PROJECTS: None.