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Technische Universität München

Communication Networks Modeling and Optimization

Lecturer (assistant)
Fidan Mehmeti [L]
Wolfgang Kellerer
Alexander Wietfeld
Razvan-Mihai Ursu
Number 0000000730
Type lecture with integrated exercises
Duration 4 SWS
Term Sommersemester 2024
Language of instruction English
Position within curricula See TUMonline
Dates See TUMonline

Admission information

See TUMonline

Objectives

Upon successful completion of the module, students are able to understand and apply analytical tools that can be used in modeling the network operation (both wireless and wireline) and its optimization. They are able to formulate optimization problems in different solvers.

Description

Introduction to probability and stochastic processes. Discrete-time Markov chains. Continuous-time Markov chains. Introduction to queueing theory. M/G/1 queues. Special queues. Queueing networks. Real-world examples. Math for the Internet architecture. Statistical multiplexing and packet buffering. Scheduling. Network optimization problems. Power optimization application.

Prerequisites

The knowledge of following modules are recommended:
- Data Networking

Teaching and learning methods

During the lectures students are instructed in a teacher-centered style. In the exercises, analytical problems will be solved. Also, students will have access to software for solving optimization problems (optimization solvers) and will be guided through several examples. Students will need to solve analytical problem on their own.

Examination

The module examination consists of a graded written exam of 120 minutes duration and homework problems (6-7 sets). In the written exam, students demonstrate their analytical skills acquired in this course by solving problems related to modeling and optimization of communication networks. Students also demonstrate their ability in deeper understanding different stochastic processes.
In the homework the students demonstrate their practical skills acquired in the course related to their capability for implementing original solutions and comparing them against other approaches by using different optimization tools (CVX, Gurobi, etc.). The homework will consist of 6-7 programming assignments to be solved using available optimization software tools.

The final grade is composed of the following components:
- 60% final exam
- 40% from homework

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