Abstract-In this paper we consider the problem of maximizing wireless network capacity (a.k.a. one-shot scheduling) in both the protocol and physical models. We give the first distributed algorithms with provable guarantees in the physical model, and show how they can be generalized to more complicated metrics and settings in which the physical assumptions are slightly violated. We also give the first algorithms in the protocol model that do not assume transmitters can coordinate with their neighbors in the interference graph, so every transmitter chooses whether to broadcast based purely on local events. Our techniques draw heavily from algorithmic game theory and machine learning theory, even though our goal is a distributed algorithm. Indeed, our main results allow every transmitter to run any algorithm it wants, so long as its algorithm has a learning-theoretic property known as no-regret in a game-theoretic setting.
In REF , the author provides the first distributed algorithm with provable guarantee based on game theory to solve the one-slot scheduling problem in both protocol and physical models.
Distributed Algorithms for Approximating Wireless Network Capacity
2010 Proceedings IEEE INFOCOM
2010 Proceedings IEEE INFOCOM
Computer Science