Jamming Bandits

TL;DR

This paper introduces a novel multi-armed bandit framework for developing adaptive, energy-efficient jamming strategies in electronic warfare, demonstrating fast convergence and effectiveness against various transmitter-receiver configurations.

Contribution

It presents new online learning algorithms for cognitive jamming that adaptively optimize physical layer parameters with proven convergence and efficiency in dynamic wireless environments.

Findings

Algorithms converge to optimal jamming strategies

Learning is faster than existing reinforcement learning methods

Effective against both static and adaptive targets

Abstract

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 adaptively and optimally 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 the 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 and prove that our learning algorithm converges to the optimal (in terms of the error rate inflicted at the victim and the energy used) 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 in comparison to existing reinforcement learning algorithms, which is particularly important in dynamically changing wireless environments. Also, we characterize the performance of the proposed bandit-based learning algorithm against multiple static and adaptive transmitter-receiver pairs.