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

Bui Minh Tuan; Van-Dinh Nguyen; Diep N. Nguyen; Nguyen Linh Trung; Nguyen Van Huynh; Dinh Thai Hoang; Marwan Krunz; Eryk Dutkiewicz

cs.LGMar 5, 2026

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

Authors: Bui Minh Tuan, Van-Dinh Nguyen, Diep N. Nguyen, Nguyen Linh Trung, Nguyen Van Huynh, Dinh Thai Hoang, Marwan Krunz, Eryk Dutkiewicz

Organizations: the School of Electrical and Data Engineering, University of Technology Sydney, NSW 2007, Australia · the School of Computer Science and Statistics, Trinity College Dublin, Dublin 2, D02PN40, Ireland · Smart Green Transformation Center (GREEN-X), VinUniversity, Hanoi, Vietnam · the Faculty of Electronics and Telecommunications, University of Engineering and Technology, Vietnam National University, Hanoi, Vietnam ·

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.

Figures & tables

Work	ISAC	Eve CSI	Sensing Role	Uncertainty Handling	MC	Method
[ 3 , 4 , 12 ]	Yes	No (AoA only)	Region/angle-aided design	Sensitive to AoA errors; limited robustness	No	Optimization
[ 10 , 18 , 19 ]	Yes	Yes / bounded	Joint waveform/beamforming	Typically assumes perfect or bounded CSI; limited angular uncertainty treatment	Limited	SDR/FP/AO
[ [ 14 , 15 , 16 ]	No	Bounded/statistical	Not considered	Robust to user CSI errors; not ISAC sensing-guided	Yes	Robust opt.
[ 22 , 23 ]	Partial	Imperfect CSI	Learning/prediction	Data-driven CSI robustness; limited CRLB-guaranteed sensing constraints	Yes	Deep learning
This work	Yes	No	Radar-echo-guided jamming	CSI & angular uncertainty with CRLB constraints	Yes	Learning + TT-Q

TABLE I : Comparison between representative secure ISAC approaches and the proposed method.

Fig. 1 : Two multicarrier ISAC architectures considered in this work. In the overlapping scheme, all subcarriers are jointly used for communication, sensing, and friendly jamming. In the non-overlapping scheme, the subcarriers are partitioned into disjoint sets, with one set allocated to communication and the remaining set used for sensing and friendly jamming.

Fig. 2 : ISAC FJ workflow: (1) message encoding, (2) beamforming generation, (3) power-normalized transmission, (4) radar echo acquisition, (5) Fisher information estimation, (6) friendly jamming optimization, and (7) message decoding. The dashed arrow highlights the sensing feedback loop, which enables adaptive jamming without requiring explicit eavesdropper CSI.

\begin{split}R_{k}^{(n)}&=\bar{\tau}\log_{2}\Bigg(1+\frac{\xi_{k}^{(n)}\left|(\mathbf{h}_{k}^{(n)})^{H}\mathbf{f}_{k}^{(n)}\right|^{2}}{\sum\limits_{j\neq k}\xi_{j}^{(n)}\left|(\mathbf{h}_{k}^{(n)})^{H}\mathbf{f}_{j}^{(n)}\right|^{2}+\zeta^{(n)}\left|(\mathbf{h}_{k}^{(n)})^{H}\mathbf{v}^{(n)}\right|^{2}+\sigma_{c}^{2}}\Bigg),\\ R_{E,k}^{(n)}&=\bar{\tau}\log_{2}\det\Big(\mathbf{I}+\left(\zeta^{(n)}\mathbf{H}_{E}^{(n)H}\mathbf{v}^{(n)}\mathbf{v}^{(n)H}\mathbf{H}_{E}^{(n)}+\sigma_{E}^{2}\mathbf{I}\right)^{-1}\mathbf{H}_{E}^{(n)H}\mathbf{f}_{k}^{(n)}\mathbf{f}_{k}^{(n)H}\mathbf{H}_{E}^{(n)}\Big).\end{split}

(28)

Table 4

\begin{split}R_{k}^{(n)}&=\bar{\tau}\log_{2}\Bigg(1+\frac{\gamma_{k}^{(n)}\left|(\mathbf{h}_{k}^{(n)})^{H}\mathbf{f}_{k}^{(n)}\right|^{2}}{\sum\limits_{j\neq k}\gamma_{j}^{(n)}\left|(\mathbf{h}_{k}^{(n)})^{H}\mathbf{f}_{j}^{(n)}\right|^{2}+\zeta^{(n)}\left|(\mathbf{h}_{k}^{(n)})^{H}\mathbf{v}^{(n)}\right|^{2}+\sigma_{c}^{2}}\Bigg),\\ R_{E,k}^{(n)}&=\bar{\tau}\log_{2}\det\Bigg(\mathbf{I}+\Big(\zeta^{(n)}\mathbf{H}_{E}^{(n)H}\mathbf{v}^{(n)}\mathbf{v}^{(n)H}\mathbf{H}_{E}^{(n)}+\sigma_{E}^{2}\mathbf{I}\Big)^{-1}\mathbf{H}_{E}^{(n)H}\mathbf{f}_{k}^{(n)}\mathbf{f}_{k}^{(n)H}\mathbf{H}_{E}^{(n)}\Bigg).\end{split}

(41)

Table 5

Parameter	Value
$N_{t}$ , $N_{r}$ , $N_{e}$	16, 4, 2
Number of users, $K$	2
Number of subcarriers, $N$	64
Set of comm subcarriers, $\mathcal{N}_{c}$	First 32 subcarriers (1–32)
Set of sensing/jamming, $\mathcal{N}_{s}$	Remaining 32 subcarriers (33–64)
Modulation scheme	QPSK ( $M=4$ )

TABLE II : Simulation Parameters

Fig. 3 : Sum secrecy rate comparison with perfect AoA estimation.

Fig. 4 : BLER on the legitimate channel.

Fig. 5 : Convergence of training process.

Fig. 6 : Average secrecy rate versus CRLB.

Fig. 7 : Average secrecy rate in worst case per user’s channel.

Fig. 8 : Average secrecy rate versus SNR with different perturbation levels of estimation.

Fig. 9 : Comprehensive comparison of the average secrecy rates between FC encoder and quantised TT.

Fig. 10 : Comparison of average secrecy rates and training loss between FC and TT method.

Metric	FC Encoder	DTTE	Pure TT Encoder
Architecture	Multi-layer FC network	TT core + FC head	Single TT-matrix layer
Compression	None	Hybrid TT + FC	Full TT decomposition
Parameters ( $N_{t}{=}16$ , $K{=}2$ )	62,563	28,371	2,800
FLOPs (Inference)	$\approx$ 125,000	$\approx$ 57,000	$\approx$ 5,600
Secrecy Rate	Highest (baseline)	Near-baseline	Lower due to overcompression
Inference Time	Slowest	Moderate	Fastest

TABLE III: Comparison of Encoder Architectures: FC, DTTE, and Pure TT

Fig. 11 : Average secrecy rate versus SNR in different levels of non-overlapping.

Fig. 12: Secrecy rate degradation, defined as the loss relative to the ideal hardware baseline.

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Wireless communications studies how information is sent over radio links and how networks manage signals, spectrum, and resources reliably.

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Short-Length Code Designs for Integrated Sensing and Communications: A Deep Learning Approach

Integrated sensing and communication (ISAC) enables joint communication and sensing using a shared waveform, but its signal design is challenging due to the inherent trade-off between the two objectives, particularly in the short blocklength regime. This paper proposes an autoencoder (AE)-based framework for ISAC waveform design in noncoherent settings. We derive a modified Cramér-Rao bound for multi-target delay estimation and analyze the maximum-likelihood decoding rule for noncoherent communication under correlated fading. These results reveal structural connections and trade-offs between communication and sensing objectives in waveform design. Based on this analysis, the AE learns waveform representations that jointly optimize both functionalities, with a tunable parameter controlling the trade-off. Simulation results show that the proposed design outperforms conventional schemes in both communication reliability and sensing accuracy, especially under short blocklength and fading conditions.

Muah Kim, Shuangyang Li, Tayyebeh Jahani-Nezhad +2

Faculty of Electrical and Computer Engineering, Dresden University of Technology, Dresden 01069, Germany · Faculty of Electrical Engineering and Computer Science, Technical University of Berlin, Berlin 10587, Germany

Integrated Sensing and Communication Wireless Communications

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Aug 4, 2026

Task-Oriented Candidate-Latent Feedback for Coarse-to-Fine Sensing in Distributed OFDM-ISAC Networks

Future integrated sensing and communication (ISAC) architectures separate the sensing entity (SE) that acquires measurements from the sensing function (SF) that performs inference, creating a need for compact, task-oriented feedback on the SE-SF interface. Forwarding the raw channel frequency response or full per-link delay-Doppler-azimuth-elevation (DDAE) tensor is prohibitively expensive, while peak-only reporting discards target-discriminative structure under clutter. We propose a learning-based coarse-to-fine sensing pipeline with candidate-latent feedback for single-target estimation. At the SE, a lightweight convolutional scorer produces a dense delay-Doppler proposal map from pilot-based OFDM channel estimates, and a learned encoder constructs K compact C-dimensional candidate tokens by fusing per-candidate azimuth-elevation patches, normalized position, and confidence cues. The latents are uniformly quantized post-training to b bits and transmitted under a finite budget B_fb = bKC + 18K + 16 bits to the SF, which performs cross-candidate refinement, reranking, and joint four-parameter estimation. On a ray-traced urban scene with static and dynamic clutter, three operating points in the (K, C, b) design space achieve 96.33-98.88% detection at 107-806 bytes per coherent processing interval, compression ratios of 1.2-9.2 x 10^4 over the 8-bit DDAE magnitude tensor, reducing the SE-SF interface from multi-Gbit/s to sub-Mbit/s rates. Cross-scene evaluation on an independent campus-scale environment achieves 98.79-99.50% detection and at-or-better angular accuracy without retraining, indicating that the learned representation captures target-relevant structure that transports across scenes of comparable or lower clutter density.

Shiv Shankar, Radha Krishna Ganti, J Klutto Milleth

Indian Institute of Technology (IIT), Madras, Chennai India · Centre of Excellence in Wireless Technology (CEWiT), Chennai India

Integrated Sensing and Communication

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Sep 25, 2026

Adaptive Pilot Selection for Unified Semantic Communication and Semantic Sensing in ISAC

Semantic communication (SemCom) and integrated sensing and communication (ISAC) are promising technologies for future 6G wireless networks. Existing studies have applied semantic technology to either the communication module or the sensing module of ISAC. In this work, we propose SemISAC, which performs both SemCom and semantic sensing within a single dual-function waveform. SemISAC uses a joint semantic encoder that extracts task-specific information for both communication and sensing. We evaluate SemISAC in a vehicular scenario in which vehicles share pixel-wise segmentation of the road environment and, through sensing, classify surrounding objects and estimate their ranges. On the transmitter side, a deep learning encoder converts the input road-scene image into semantic symbols and places them on the data cells of an OFDM grid, while the remaining cells serve as pilots for channel state information estimation and sensing. The pilot configuration is adaptively optimized based on the channel conditions to balance communication and sensing requirements. At the receiver, a deep learning model reconstructs the segmentation from the received waveform, while the transmitting vehicle captures the reflected waveforms from surrounding objects and uses task-specific deep learning decoders for target recognition and range estimation. Simulation results show that SemISAC achieves a segmentation accuracy close to that of the dedicated SemCom module while outperforming both conventional and semantic baselines in target recognition and range estimation.

Muhammad Abubakar Rashid, Muhammad Hannan Akram, Haejoon Jung +1

School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Islamabad, Pakistan · Department of Electronic Engineering, Kyung Hee University, Yongin 17104, Republic of Korea

Integrated Sensing and Communication Task-Oriented Semantic Communication

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