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Analysis of Communication Networks2024HT
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Course plan
No of lectures
It is expected to have 9 lectures.
Recommended for
Advanced master students and PhD students.
The course was last given
2023HT
Goals
The students will learn mathematical methods for the efficient design of communication networks, using control, optimization, and stochastic network theories.
Prerequisites
A basic background on probability theory and stochastic processes.
Organization
The first part will cover the fundamentals and the second part will cover more advanced topics.
Content
Optimization formulation of network resource allocation, convergence analysis
of primal and dual algorithms, Delay equations and applications to the study of
congestion control algorithm; Interpretation of network architecture and
algorithms in terms of optimization solution; Game-theoretic interpretation of
optimization formulation and solution.
Mathematical tools: Markov chains and discrete-time queueing theory;
Statistical multiplexing and large deviations.
Scheduling algorithms for switches and wireless networks: Maxweight scheduling,
complexity, and distributed randomized algorithms, statistical physics
techniques.
Other topics as time permits and there is interest: Modeling P2P networks,
Cloud Computing, etc.
Literature
R. Srikant and L. Ying, Communication Networks: An Optimization, Control and Stochastic Networks Perspective, Cambridge University Press, 2014.
Lectures
One lecture is expected for each item from the content above.
We will have a final day with the presentations of the projects, or the exam.
Examination
A project-based examination where each student can analyze and implement a topic relevant to this book. A presentation is expected along with a written report. There is also a possibility for a written exam or oral examination on the white board. This depends on the amount of students and their preference.
Examiner
Nikolaos Pappas
Credits
6 ECTS
Comments
Page responsible: Director of Graduate Studies
