Timely NextG Communications with Decoy Assistance against Deep Learning-based Jamming

TL;DR

This paper proposes a decoy-based anti-jamming strategy for NextG communications to protect against deep learning-enabled jammers, ensuring timely and reliable information transfer.

Contribution

It introduces a novel decoy-assisted approach to mitigate deep learning-based jamming and analyzes its effectiveness on information freshness and reliability.

Findings

Decoy strategy confuses deep learning jammers, reducing successful interference.

Power control impacts both transmission success and jammer detection accuracy.

The approach effectively maintains information freshness under jamming attacks.

Abstract

We consider the transfer of time-sensitive information in next-generation (NextG) communication systems in the presence of a deep learning based eavesdropper capable of jamming detected transmissions, subject to an average power budget. A decoy-based anti-jamming strategy is presented to confuse a jammer, causing it to waste power when disrupting decoy messages instead of real messages. We investigate the effectiveness of the anti-jamming strategy to guarantee timeliness of NextG communications in addition to reliability objectives, analyzing the Age of Information subject to jamming and channel effects. We assess the effect of power control, which determines the success of a transmission but also affects the accuracy of the adversary's detection, making it more likely for the jammer to successfully identify and jam the communication. The results demonstrate the feasibility of mitigating eavesdropping and jamming attacks in NextG communications with information freshness objectives using a decoy to guarantee timely information transfer.