A Cognitive Tuning of Contention Windows in WiFi Infrastructure Networks

In WiFi networks, mobile nodes compete for accessing a shared channel by means of a random access protocol called Distributed Coordination Function (DCF). Although this protocol is in principle fair, since it should guarantee that all the stations have the same probability to transmit on the channel, it has been shown that unfair behaviors may emerge in actual networking scenarios. These phenomena are due to different reasons, including non-standard configurations of the nodes, critical network topologies, and short-term performance observations. In this paper we propose a game-theoretic approach for defining an enhanced DCF scheme suitable for WiFi intelligent nodes employing cognitive functionalities. We assume that a cognitive WiFi node can dynamically change its strategy, by rationally tuning its contention window on the basis of channel observations. We prove that, for infrastructure networks with bidirectional traffic and homogeneous application requirements, our scheme is able to reach equilibrium conditions, which are also Pareto optimal. Specifically, we show that the station strategies converge toward values which maximize a per-node utility function, while maintaining performance fairness.