Deep Learning-Driven Friendly Jamming for Secure Multicarrier ISAC Under Channel Uncertainty
This work addresses the critical problem of enhancing physical-layer security in ISAC systems for wireless communication and radar sensing, particularly for scenarios with unknown eavesdropper locations and channel uncertainties, which is a significant challenge for practical ISAC deployments.
This paper proposes a deep learning framework for secure multicarrier Integrated Sensing and Communication (ISAC) systems, which enhances physical-layer security by guiding directional jamming using radar echo feedback, even with unknown eavesdropper locations and imperfect channel state information. The framework achieves significant improvements in secrecy rate and reduced block error rate (BLER) while demonstrating strong robustness against channel and angular estimation errors.
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.