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Game Theory for Wireless Engineers
Abstract
The application of mathematical analysis to wireless networks has met with limited success, due to the complexity of mobility and traffic models, coupled with the dynamic topology and the unpredictability of link quality that characterize such networks. The ability to model individual, independent decision makers whose actions potentially affect all other decision makers makes game theory particularly attractive to analyze the performance of ad hoc networks. Game theory is a field of applied mathematics that describes and analyzes interactive decision situations. It consists of a set of analytical tools that predict the outcome of complex interactions among rational entities, where rationality demands a strict adherence to a strategy based on perceived or measured results. In the early to mid-1990's, game theory was applied to networking problems including flow control, congestion control, routing and pricing of Internet services. More recently, there has been growing interest in adopting game-theoretic methods to model today's leading communications and networking issues, including power control and resource sharing in wireless and peer-to-peer networks. This work presents fundamental results in game theory and their application to wireless communications and networking. We discuss normal-form, repeated, and Markov games with examples selected from the literature. We also describe ways in which learning can be modeled in game theory, with direct applications to the emerging field of cognitive radio. Finally, we discuss challenges and limitations in the application of game theory to the analysis of wireless systems. We do not assume familiarity with game theory. We introduce major game theoretic models and discuss applications of game theory including medium access, routing, energy-efficient protocols, and others. We seek to provide the reader with a foundational understanding of the current research on game theory applied to wireless communications and networking.
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Cites
Prospect Theory: An Analysis of Decision under Risk
Kahneman, D., & Tversky, A. (1979) · Econometrica · doi:10.2307/1914185 · Cited by 48,524
“Risk, Ambiguity, and the Savage Axioms.”
Ellsberg, D. (1961) · The Quarterly Journal of Economics · doi:10.2307/1884324 · Cited by 7,891
Cited by
Coalitional game theory for communication networks
Saad, W., Han, Z., Debbah, M., Hjorungnes, A., & Basar, T. (2009) · IEEE Signal Processing Magazine · doi:10.1109/msp.2009.000000 · Cited by 1,051
Game Theory for Multi-Access Edge Computing: Survey, Use Cases, and Future Trends
Moura, J., & Hutchison, D. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2863030 · Cited by 193
Self-organization of nodes in mobile ad hoc networks using evolutionary games and genetic algorithms
Kusyk, J., Sahin, C. S., Umit Uyar, M., Urrea, E., & Gundry, S. (2011) · Journal of Advanced Research · doi:10.1016/j.jare.2011.04.006 · Cited by 45
Metadata
- Authors
- MacKenzie, A. B., & DaSilva, L. A.
- Venue
- Synthesis lectures on communications
- Publisher
- Morgan & Claypool Publishers
- Published
- 2006-01-01
- Volume
- 1
- Issue
- 1
- Pages
- 1-86
- ISSN
- 1932-1244 (ISSN-L)
- OpenAlex
- W2018932238
- Cited by
- 390
- References
- 2
- FWCI
- 34.18
- Type
- journal article
- Language
- en
- Open access
- closed
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- MacKenzie, A. B., & DaSilva, L. A. (2006). Game Theory for Wireless Engineers. Synthesis lectures on communications. https://doi.org/10.2200/s00014ed1v01y200508com001
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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