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Jamming-Resistant Learning in Wireless Networks

  • Johannes Dams
  • Martin Hoefer
  • Thomas Kesselheim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8573)

Abstract

We consider capacity maximization in wireless networks under adversarial interference conditions. There are n links, each consisting of a sender and a receiver, which repeatedly try to perform a successful transmission. In each time step, the success of attempted transmissions depends on interference conditions, which are captured by an interference model (e.g. the SINR model). Additionally, an adversarial jammer can render a (1 − δ)-fraction of time steps unsuccessful. For this scenario, we analyze a framework for distributed no-regret learning algorithms. We obtain an \(O\left(1/\delta\right)\)-approximation for the problem of maximizing the number of successful transmissions. Our approach provides even a constant-factor approximation when the jammer exactly blocks a (1 − δ)-fraction of time steps. In addition, we consider the parameters of the jammer being unknown to the algorithm, and we also consider a stochastic jammer, for which we obtain a constant-factor approximation after a polynomial number of time steps. We extend our results to more general settings, in which links arrive and depart dynamically.

Keywords

Wireless Network Time Slot Capacity Maximization Successful Transmission Approximation Factor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Johannes Dams
    • 1
  • Martin Hoefer
    • 2
  • Thomas Kesselheim
    • 3
  1. 1.Dept. of Computer ScienceRWTH Aachen UniversityGermany
  2. 2.Max-Planck-Institut für Informatik and Saarland UniversityGermany
  3. 3.Dept. of Computer ScienceCornell UniversityUSA

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