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

2026-03-05

AI Summary

This work addresses physical-layer security in multi-carrier integrated sensing and communication (ISAC) systems under imperfect channel state information and unknown eavesdropper locations. The authors propose a deep learning-based approach that requires no prior knowledge of the eavesdropper’s channel or angular information, leveraging radar echo feedback to guide directional jamming and jointly optimize beamforming and friendly interference. Key innovations include a non-parametric Fisher information matrix estimator based on f-divergence, constrained by the Cramér-Rao lower bound to enhance angular estimation robustness; a quantized tensor-train encoder that drastically reduces model size; and a non-overlapping secure subband mechanism. The proposed scheme significantly improves secrecy rate and reduces block error rate while achieving over 100× model compression with negligible performance loss, maintaining strong robustness against channel uncertainty and angular estimation errors.

Technology Category

• Search and Optimization: Learning to Search
• Intelligent Robots: State Estimation
• Machine Learning: Hardware-aware ML

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Integrated Sensing and Communication
• Physical-layer Security
• Channel Uncertainty
• Friendly Jamming
• Eavesdropper Location

Innovation

• Deep Learning-Driven Friendly Jamming
• Integrated Sensing and Communication (ISAC)
• Fisher Information Matrix (FIM)
• Tensor Train Compression
• Physical-Layer Security