Sleeping Experts in Wireless Networks

We consider capacity maximization algorithms for wireless networks with changing availabilities of spectrum. There are n senderreceiver pairs (called links) and k channels. We consider an iterative round-based scenario, where in each round the set of channels available to each link changes. Each link independently decides about access to one available channel in order to implement a successful transmission. Transmissions are subject to interference and noise, and we use a general approach based on affectance to define which attempts are successful. This includes recently popular interference models based on SINR. Our main result is that efficient distributed algorithms from sleepingexpert regret learning can be used to obtain constant-factor approximations if channel availability is stochastic and independently distributed among links. In general, sublinear approximation factors cannot be obtained without the assumption of stochastic independence among links. A direct application of the no-external regret property is not sufficient to guarantee small approximation factors.