English

If sufficient demand

The demand for wireless communications is growing with a tremendous pace. Game theory has shown to be a robust tool to address problems with resource assignment. The course aims to highlight applications in wireless communication by classifying game-theory methods with regards to the specific use cases of resource sharing in wireless networks, ranging from power assignment and channel allocation to medium access control and transmission scheduling. For the identified cases, the course also aims to enable the doctoral students to undertake the study of selfish behaviour and to choose and apply suitable incentive schemes to enforce cooperation among participating network entities to achieve globally efficient resource sharing.

1. Resource allocation problems in wireless networks: cellular and broadband, wireless local area networks, multi-hop networks, cognitive radio networks
2. Game theory in a nutshell: definitions, taxonomy of game types, solution concepts, solution efficiency, learning in games, useful game structures, example resource assignment games in wireless communications
3. Incentive schemes: concepts, taxonomy, case studies from wireless communications
4. Mechanism design: concepts, auctions, case studies from wireless communications
5. Cooperative game theory: coalition formation, bargaining protocols, selected problems from wireless communications
6. Empirical game-theoretic analysis: simulation methods, model complexity, convergence, communication and computational costs

be able to understand the criteria that must be met in order to decide if game theoretic modelling is in general applicable to a given resource assignment problem.
show a profound understanding of both game theory and resource assignment in wireless networks, and the situation where conflicting demands arise.
understand the criteria for selecting suitable game theoretic methods for the various resource allocation problems in wireless networks.
demonstrate knowledge about the analysis of game theoretic models with regards to various solution concepts (e.g. Nash equilibrium, correlated equilibrium, Pareto efficiency, etc.) which are applicable to various resource assignment problems.
understand cooperative game modelling and incentive schemes useful for achieving desired resource assignment solutions.

be able to independently model resource assignment problems using game theory and to critically analyze the solution concept(s) for the developed models.
show the ability to evaluate the quality of alternative solutions to the same problem, and to design appropriate mechanisms to achieve the desired solution(s).
be able to develop appropriate algorithms to implement the models or mechanisms of interest.

be able to take on appropriate approaches to critically analyze resource allocation problems in wireless communications using suitable game theoretic models and meaningful solution concepts.
demonstrate the ability to distinguish efficient solutions from a set of solutions, with regards to various notions of solution efficiency.
be able to assess the behaviour of the used models under realistic settings, and identify the potential bottlenecks that may arise under such situations.

Lectures

Exercises

Project

Written report

Written assignments

Seminars given by participants

Miscellaneous

The doctoral students are also required to do a mini-project as part of course requirements.

Failed, pass

The doctoral students should be fluent in standard mathematics, basics of game theory, and fundamental concepts of wireless communications.

Roughgarden, T., Tardos, E. & Vazirani, Vijay V.: Algorithmic Game Theory. Cambridge University Press, 2007.
Boche, H., Honig, Michael L. & Poor, H. Vincent: Mechanisms and Games for Dynamic Spectrum Allocation. Cambridge University Press, 2014.
Niyato, D., Saad, W., Baar, T. & Hjrungnes, A.: Game Theory in Wireless and Communication Networks: Theory, Models, and Applications. Cambridge University Press, 2012.

Other selected materials from literature.

Course coordinator: Saeed Bastani, saeed.bastani@eit.lth.se

EIT125F

 -05-18

FN1/Anders Gustafsson