Jamming-Resistant Learning in Wireless Networks

We consider capacity maximization in wireless networks under adversarial interference conditions. There are n links, i.e., sender-receiver pairs, 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-δ)-fraction of time steps in a time window unsuccessful. For this scenario, we analyze a framework for distributed no-regret learning algorithms to get provable approximation guarantees. We obtain an O(1/δ)-approximation for the problem of maximizing the number of successful transmissions. Our approach provides even a constant-factor approximation when the jammer exactly blocks a (1-δ)-fraction of time steps. In addition, we consider the parameters of the jammer being partially unknown to the algorithm, and we also consider a stochastic jammer, for which we obtain a constant-factor approximation after a polynomial number of time steps. We extend our results to 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.