**Master Course Description**

**No:** EE 416

**Title:** Random Signals for
Communications and Signal Processing

**Credits:** 4

**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:

*Calculate*the probability of combinations of events using hand and computer analysis.*Write*computer (MATLAB) programs to compute many probability distributions.*Solve*for the distributions of random variable arising from certain functions of random variables.*Model*datasets arising in communications using common probabilistic models.*Analyze*the effect of randomness on communication signals.*Model and analyze*the linear systems using multivariate Gaussian distributions.*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:**

- Linear Systems Theory in Discrete and Continuous Time
- Basic Signals in Discrete and Continuous Time
- Differential and Integral Calculus
- Principles of Engineering Statistics
- Principles of Probability
- Facility with MATLAB

**Topics:**

- Models of Probability (2 weeks)
- Single Random Variables (2 weeks)
- Two Random Variables (2 weeks)
- Multiple Random Variables (2 weeks)
- 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