Deep Learning-Driven Friendly Jamming for Secure Multicarrier ISAC Under Channel Uncertainty

TL;DR

This paper introduces a deep learning-based friendly jamming framework for secure multicarrier ISAC systems that operates effectively under channel uncertainty and unknown eavesdropper locations, improving secrecy and robustness.

Contribution

It proposes a radar-aware neural network with a novel FIM estimator and a tensor train encoder to optimize jamming without Eve's explicit CSI, enhancing security in practical ISAC scenarios.

Findings

Significant secrecy rate improvements demonstrated.

Robustness against CSI and AoA estimation errors confirmed.

Model size reduced by over 100 times with negligible performance loss.

Abstract

Integrated sensing and communication (ISAC) systems promise efficient spectrum utilization by jointly supporting radar sensing and wireless communication. This paper presents a deep learning-driven framework for enhancing physical-layer security in multicarrier ISAC systems under imperfect channel state information (CSI) and in the presence of unknown eavesdropper (Eve) locations. Unlike conventional ISAC-based friendly jamming (FJ) approaches that require Eve's CSI or precise angle-of-arrival (AoA) estimates, our method exploits radar echo feedback to guide directional jamming without explicit Eve's information. To enhance robustness to radar sensing uncertainty, we propose a radar-aware neural network that jointly optimizes beamforming and jamming by integrating a novel nonparametric Fisher Information Matrix (FIM) estimator based on f-divergence. The jamming design satisfies the Cramer-Rao lower bound (CRLB) constraints even in the presence of noisy AoA. For efficient implementation, we introduce a quantized tensor train-based encoder that reduces the model size by more than 100 times with negligible performance loss. We also integrate a non-overlapping secure scheme into the proposed framework, in which specific sub-bands can be dedicated solely to communication. Extensive simulations demonstrate that the proposed solution achieves significant improvements in secrecy rate, reduced block error rate (BLER), and strong robustness against CSI uncertainty and angular estimation errors, underscoring the effectiveness of the proposed deep learning-driven friendly jamming framework under practical ISAC impairments.