English

If sufficient demand

The aim of this course is to provide students with the tools and mentality of stochastic optimization. Communication networks in practice always contain a certain level of randomness and uncertainty. This random behavior naturally necessities the stochastic investigation and design of these networks, where the classical deterministic optimization tools fail to be applied. This course intents to present well-known stochastic optimization techniques with their applications to communication systems.

Optimization tools for stochastic communication networks covering both classic results and current research. Sample application areas: queuing-theoretic problems, network flow problems, network resource allocation and utility maximization, wireless network power control, medium access control, routing. Sample optimization tools: KKT optimality condition, dynamic programming, stochastic approximation, Lyapunov optimization.

be able to understand how to approach different optimization problems.
be able to understand the mentality between different optimization techniques.
be able to describe and use the most suitable optimization technique for a given problem.

be able to define, model and formulate a given stochastic optimization problem.
be able to analytical solve a given problem with the appropriate optimization tools.
be able to elaborate and use the tools to analyze the related problems in his/her research area.

be able to have a comprehensive view of stochastic optimization tools.
be able to judge how difficult to solve a given optimization problem and its complexity.
be able to understand the advantages and drawbacks of using a particular optimization technique for a given problem.

Lectures

Exercises

Self-study literature review

Written exam

Written assignments

Seminars given by participants

There will be one written exam. There will be also a final project which is closely related to student's research. The final project will be presented in the class. There will be 2-3 assignments and paper readings.

Failed, pass

Basic engineering mathematics. Basic probability theory.

Scientific papers.
L. Georgiadis, M. J. Neely & Tassiulas, L.: Resource allocation and cross-layer control in wireless networks. Foundations and Trends(r) in Networking, 2006.
Shakkottai, S. & Srikant, R.: Network Optimization and Control. Foundations and Trends(r) in Networking, 2008.

Course Coordinator: Mehmet Karaca, mehmet.karaca@eit.lth.se

EIT135F

2016-06-21

Rektor Viktor Öwall