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Repeated Game Analysis of a CSMA/CA Network under a Backoff Attack.

Juan Parras1, Santiago Zazo1

  • 1Information Processing and Telecommunications Center, Universidad Politécnica de Madrid, ETSI Telecomunicación, Av. Complutense 30, 28040 Madrid, Spain.

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Summary

Deviations in wireless networks using Carrier Sense Medium Access with Collision Avoidance (CSMA/CA) reduce fairness. This study uses game theory to find solutions for improved resource sharing in these networks.

Keywords:
CSMA/CAFolk theorembackoff attackcorrelated equilibriumrepeated gamesubgame perfect equilibrium

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Area of Science:

  • Computer Science
  • Network Engineering
  • Game Theory

Background:

  • Wireless networks often use Carrier Sense Medium Access with Collision Avoidance (CSMA/CA) for channel access.
  • Deviations from the standard CSMA/CA protocol by some stations can negatively impact network performance and fairness.
  • Previous work modeled this scenario using static game theory.

Purpose of the Study:

  • To analyze the impact of protocol deviations in CSMA/CA wireless networks.
  • To investigate the use of repeated game theory to improve outcomes for all network stations.
  • To develop and validate analytical and distributed algorithms for learning equilibrium strategies.

Main Methods:

  • Utilizing Bianchi's model to analyze network throughput and fairness.
  • Applying repeated game theory, including Folk theorem concepts.
  • Deriving analytical solutions for two-player scenarios using subgame perfect and correlated equilibria.
  • Proposing a distributed algorithm for learning equilibria with multiple players.
  • Conducting numerical simulations for validation and comparison.

Main Results:

  • Protocol deviations in CSMA/CA networks significantly reduce network fairness.
  • Deviating stations gain a disproportionately larger share of network resources.
  • Repeated game theory offers potential for improved outcomes compared to static game models.
  • Analytical and distributed algorithms provide methods for identifying and achieving equilibria.

Conclusions:

  • Deviations in CSMA/CA protocols severely compromise network fairness.
  • Repeated game theory and equilibrium concepts offer viable solutions for mitigating unfairness.
  • The proposed distributed algorithm is effective for learning equilibria in multi-player scenarios.
  • Numerical simulations confirm the effectiveness of the proposed analytical and algorithmic approaches.