Linear Jamming Bandits: Sample-Efficient Learning for Non-Coherent Digital Jamming

TL;DR

This paper introduces a linear bandit algorithm tailored for non-coherent digital jamming, significantly improving sample efficiency and convergence speed by leveraging action similarities and context features, with the ability to incorporate prior knowledge.

Contribution

The work presents a novel linear bandit approach for digital jamming that enhances learning efficiency and convergence, addressing limitations of previous methods.

Findings

Improved convergence over prior algorithms.

Effective integration of prior knowledge.

Enhanced sample efficiency in jamming scenarios.

Abstract

It has been shown (Amuru et al. 2015) that online learning algorithms can be effectively used to select optimal physical layer parameters for jamming against digital modulation schemes without a priori knowledge of the victim's transmission strategy. However, this learning problem involves solving a multi-armed bandit problem with a mixed action space that can grow very large. As a result, convergence to the optimal jamming strategy can be slow, especially when the victim and jammer's symbols are not perfectly synchronized. In this work, we remedy the sample efficiency issues by introducing a linear bandit algorithm that accounts for inherent similarities between actions. Further, we propose context features which are well-suited for the statistical features of the non-coherent jamming problem and demonstrate significantly improved convergence behavior compared to the prior art. Additionally, we show how prior knowledge about the victim's transmissions can be seamlessly integrated into the learning framework. We finally discuss limitations in the asymptotic regime.