A systematic learning method for optimal jamming

Can an intelligent jammer learn and adapt to unknown environments in an electronic warfare-type scenario? In this paper, we answer this question in the positive, by developing a cognitive jammer that disrupts the communication between a victim transmitter-receiver pair. We formalize the problem using a novel multi-armed bandit framework where the jammer can choose various physical layer parameters such as signaling scheme, power level and the on-off/pulsing duration in an attempt to obtain power efficient jamming strategies. We first present novel online learning algorithms to maximize the jamming efficacy against static transmitter-receiver pairs i.e., the case when the victim does not change its communication technique despite the presence of interference. We prove that our learning algorithm converges to the optimal jamming strategy. Even more importantly, we prove that the rate of convergence to the optimal jamming strategy is sub-linear, i.e. the learning is fast, which is important in dynamically changing wireless environments. Also, we characterize the performance of the proposed bandit-based learning algorithm against adaptive transmitter-receiver pairs.