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 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.
['Michael Dinitz']
Brief announcement: distributed algorithms for approximating wireless network capacity