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 , Prentice Hall, 2 nd edition (1994)
REFERENCE TEXTS: None
COURSE INSTRUCTOR: Prof. Randall Berry
COURSE DIRECTOR: Prof. Abraham Haddad
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 control, communications, networks, and signal processing.
PREREQUISITES BY COURSES: One course in probability.
PREREQUISITES BY TOPIC:
• Probability theory.
• Frequency spectrum, Fourier transforms.
DETAILED COURSE TOPICS:
- Week 1: Review of probability theory.
- Week 2: Review of random variables.
- Week 3: Multiple random variables.
- Week 4: Limit theorems and estimation.
- Week 5: Introduction to random processes.
- Week 6: Properties of random processes.
- Week 7: Spectral properties.
- Week 8: Gaussian and Poisson processes.
- Week 9: Markov chains.
- Week 10: Basic queueing models.
COMPUTER USAGE: Optional.
LABORATORY PROJECTS: None.
- Homework – 30%
- Midterm – 30%
- Final – 40%
COURSE OBJECTIVES: When a student completes this course, s/he should be able to:
• Understand the description and behavior of random processes.
• Model and analyze systems with random signals.