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

arXiv: https://arxiv.org/abs/2603.05062 · HTML: https://mlsanity.com/paper/2603.05062/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 46 references that are in the map.
- First posted on arXiv 5 Mar 2026.

## Builds on (most cited first, 22 of 22)

- [Tensorizing Neural Networks](https://mlsanity.com/paper/1509.06569/) (2015)
- [Tensor Decomposition for Signal Processing and Machine Learning](https://mlsanity.com/paper/1607.01668/) (2016)
- [TensorLy: Tensor Learning in Python](https://mlsanity.com/paper/1610.09555/) (2016)
- [Integrated Sensing and Communications: Towards Dual-functional Wireless Networks for 6G and Beyond](https://mlsanity.com/paper/2108.07165/) (2021)
- [Integrating Sensing and Communications for Ubiquitous IoT: Applications, Trends and Challenges](https://mlsanity.com/paper/2104.11457/) (2021)
- [Joint Radar and Communication Design: Applications, State-of-the-art, and the Road Ahead](https://mlsanity.com/paper/1906.00789/) (2019)
- [Integrated Sensing and Communication with Massive MIMO: A Unified Tensor Approach for Channel and Target Parameter Estimation](https://mlsanity.com/paper/2401.01738/) (2024)
- [Empirically Estimable Classification Bounds Based on a New Divergence Measure](https://mlsanity.com/paper/1412.6534/) (2014)
- [Empirical non-parametric estimation of the Fisher Information](https://mlsanity.com/paper/1408.1182/) (2014)
- [Towards Multi-Functional 6G Wireless Networks: Integrating Sensing, Communication and Security](https://mlsanity.com/paper/2107.07735/) (2021)
- [Sensing-Assisted Eavesdropper Estimation: An ISAC Breakthrough in Physical Layer Security](https://mlsanity.com/paper/2210.08286/) (2022)
- [Secure Integrated Sensing and Communication](https://mlsanity.com/paper/2303.11350/) (2023)
- [A Novel Joint Angle-Range-Velocity Estimation Method for MIMO-OFDM ISAC Systems](https://mlsanity.com/paper/2308.03387/) (2023)
- [Secure Radar-Communication Systems with Malicious Targets: Integrating Radar, Communications and Jamming Functionalities](https://mlsanity.com/paper/1909.03873/) (2019)
- [Secure Dual-Functional Radar-Communication Transmission: Exploiting Interference for Resilience Against Target Eavesdropping](https://mlsanity.com/paper/2107.04747/) (2021)
- [Robust and Secure Resource Allocation for ISAC Systems: A Novel Optimization Framework for Variable-Length Snapshots](https://mlsanity.com/paper/2206.13307/) (2022)
- [Robust Transmit Beamforming for Secure Integrated Sensing and Communication](https://mlsanity.com/paper/2207.13863/) (2022)
- [Securing the Sensing Functionality in ISAC Networks: An Artificial Noise Design](https://mlsanity.com/paper/2312.00981/) (2023)
- [Performance Analysis and Power Allocation for Massive MIMO ISAC Systems](https://mlsanity.com/paper/2411.10723/) (2024)
- [Securing MIMO Wiretap Channel with Learning-Based Friendly Jamming under Imperfect CSI](https://mlsanity.com/paper/2312.07011/) (2023)
- [Secure Full Duplex Integrated Sensing and Communications](https://mlsanity.com/paper/2312.11367/) (2023)
- [Optimal Beamforming for Secure Integrated Sensing and Communication Exploiting Target Location Distribution](https://mlsanity.com/paper/2312.13797/) (2023)

## Abstract (arXiv)

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.