Selfish Random Access: Equilibrium Conditions and Best-Response Learning

Hazer Inaltekin; Mung Chiang; Harold Poor

Game Theory for Networks. 2nd International ICST Conference, GAMENETS 2011, Shanghai, China, April 16-18, 2011, Revised Selected Papers

Research Article

Selfish Random Access: Equilibrium Conditions and Best-Response Learning

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@INPROCEEDINGS{10.1007/978-3-642-30373-9_12,
    author={Hazer Inaltekin and Mung Chiang and Harold Poor},
    title={Selfish Random Access: Equilibrium Conditions and Best-Response Learning},
    proceedings={Game Theory for Networks. 2nd International ICST Conference, GAMENETS 2011, Shanghai, China, April 16-18, 2011, Revised Selected Papers},
    proceedings_a={GAMENETS},
    year={2012},
    month={10},
    keywords={Slotted ALOHA multipacket reception game theory contention control medium access control},
    doi={10.1007/978-3-642-30373-9_12}
}

Hazer Inaltekin
Mung Chiang
Harold Poor
Year: 2012
Selfish Random Access: Equilibrium Conditions and Best-Response Learning
GAMENETS
Springer
DOI: 10.1007/978-3-642-30373-9_12

Hazer Inaltekin¹^,*, Mung Chiang²^,*, Harold Poor²^,*

1: The University of Melbourne
2: Princeton University

*Contact email: hazeri@unimelb.edu.au, chiangm@princeton.edu, poor@princeton.edu

Abstract

This paper studies a class of random access games for wireless channels with multipacket reception. First, necessary and sufficient equilibrium conditions for a contention strategy profile to be a Nash equilibrium for general wireless channels are established. Then, applications of these equilibrium conditions for well-known channel models are illustrated. Various engineering insights and design ideas are provided. Finally, the results are extended to an incomplete information game setting, and best-response learning dynamics leading to Nash equilibria are investigated.

Keywords: Slotted ALOHA multipacket reception game theory contention control medium access control

Published: 2012-10-08

: http://dx.doi.org/10.1007/978-3-642-30373-9_12

Selfish Random Access: Equilibrium Conditions and Best-Response Learning

Abstract

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