  We consider capacity maximization in wireless networks under adversarial
interference conditions. There are n links, each consisting of a sender and a
receiver, which repeatedly try to perform a successful transmission. In each
time step, the success of attempted transmissions depends on interference
conditions, which are captured by an interference model (e.g. the SINR model).
Additionally, an adversarial jammer can render a (1-delta)-fraction of time
steps unsuccessful. For this scenario, we analyze a framework for distributed
learning algorithms to maximize the number of successful transmissions. Our
main result is an algorithm based on no-regret learning converging to an
O(1/delta)-approximation. It provides even a constant-factor approximation when
the jammer exactly blocks a (1-delta)-fraction of time steps. In addition, we
consider a stochastic jammer, for which we obtain a constant-factor
approximation after a polynomial number of time steps. We also consider more
general settings, in which links arrive and depart dynamically, and where each
sender tries to reach multiple receivers. Our algorithms perform favorably in
simulations.
