EE 381K Advanced Telecommunication Networks - Fall 2010

Large Deviations and Stochastic Network Models with Applications to Network Analysis


This course will cover diverse large deviations results, network utility maximzation and and stochastic network models and their applications to modeling and analysis of network systems. Applications will include, approches to resource allocation in communication networks, TCP-based congestion control, wireless scheduling, P2P networks, and possibly energy systems. As time permits I also intend to cover some topics in stochastic comparisons, which I think will be useful to you in your graduate studies. The material in this course is a mix of standard matarial and recent research results, as such, lectures will be drawn from standard texts in the area as well as key research papers.

Course Contents

Course web page


You will need to have taken the following graduate level courses (or have equivalent background): (1) EE 381J Probability and Random Processes; (2) EE 381 K Analysis and Design of Communication Networks, i.e., background in queueing theory and time-reversible Markov Chains; (3) background in analysis and convex optimization. The end goal is to model and analyse a variety of network systems. You should be familiar with the basic communication and wireless network systems. This is an advanced course, which for the most part should be taken by 2nd-3rd year graduate students. Expect it to be challenging but hopefully rewarding too!

Some Texts and Selected Papers

The course will cover a variety of topics drawing from various texts and research papers. Below I've listed several books some of which are available on the web. The papers to be discussed in the course are available in blackboard. I plan to follow both "Big queues" and "Network Optimization and Control" for reasonably large sections of the course.


You will be responsible for basic material presented in class and strongly encouraged to participate in class discussions. Think of this as a team effort. Your grade will be based  20% homework and class participation, 30% on two quizzes, and 50% on your presentations and project.

Where does course fit in?

This course is intended to build on your own background and interests as well as material in Probability and Random Processes, Communication Networks: Analysis and Design, Information Theory and Optimization.

Note: All departmental, college and university regulations concerning drops will be followed. The University of Texas at Austin provides upon request appropriate academic accommodations for qualified students with disabilities. For more information, contact the Office of the Dean of Students at 471-6259, 471-4641 TTY.