A Game Theoretic Approach to Distributed Opportunistic Scheduling

TL;DR

This paper introduces a game theoretic algorithm for distributed opportunistic scheduling that incentivizes honest behavior and deters selfish users by employing a punishment mechanism, ensuring optimal network operation.

Contribution

It presents a novel game theoretic algorithm with a punishment strategy to prevent selfish behavior in distributed opportunistic scheduling systems.

Findings

The algorithm drives the network to optimal operation when all stations follow it.

Selfish stations cannot profit by deviating from the proposed algorithm.

Simulations confirm the algorithm's effectiveness against selfish behavior.

Abstract

Distributed Opportunistic Scheduling (DOS) is inherently harder than conventional opportunistic scheduling due to the absence of a central entity that has knowledge of all the channel states. With DOS, stations contend for the channel using random access; after a successful contention, they measure the channel conditions and only transmit in case of a good channel, while giving up the transmission opportunity when the channel conditions are poor. The distributed nature of DOS systems makes them vulnerable to selfish users: by deviating from the protocol and using more transmission opportunities, a selfish user can gain a greater share of the wireless resources at the expense of the well-behaved users. In this paper, we address the selfishness problem in DOS from a game theoretic standpoint. We propose an algorithm that satisfies the following properties: (i) when all stations implement the algorithm, the wireless network is driven to the optimal point of operation, and (ii) one or more selfish stations cannot gain any profit by deviating from the algorithm. The key idea of the algorithm is to react to a selfish station by using a more aggressive configuration that (indirectly) punishes this station. We build on multivariable control theory to design a mechanism for punishment that on the one hand is sufficiently severe to prevent selfish behavior while on the other hand is light enough to guarantee that, in the absence of selfish behavior, the system is stable and converges to the optimum point of operation. We conduct a game theoretic analysis based on repeated games to show the algorithm's effectiveness against selfish stations. These results are confirmed by extensive simulations.