---
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标题: "Secure Cooperative THz ISAC via Mamba Empowered Graph Neural Network Precoding"
原文链接: "https://arxiv.org/abs/2608.10467"
发布日期: "2026-08-11"
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## Computer Science > Information Theory

## Title:Secure Cooperative THz ISAC via Mamba Empowered Graph Neural Network Precoding

Authors:[Chao Wang](https://arxiv.org/search/cs?searchtype=author&query=Wang,+C), [Zan Li](https://arxiv.org/search/cs?searchtype=author&query=Li,+Z), [Xiangnan Zhou](https://arxiv.org/search/cs?searchtype=author&query=Zhou,+X), [Haibin Zhang](https://arxiv.org/search/cs?searchtype=author&query=Zhang,+H), [Hao Xu](https://arxiv.org/search/cs?searchtype=author&query=Xu,+H), [Liang Jin](https://arxiv.org/search/cs?searchtype=author&query=Jin,+L), [Derrick Wing Kwan Ng](https://arxiv.org/search/cs?searchtype=author&query=Ng,+D+W+K)

[View PDF](https://arxiv.org/pdf/2608.10467) [HTML (experimental)](https://arxiv.org/html/2608.10467v1)

> Abstract:The terahertz (THz) band offers abundant spectrum resources for high-throughput communication and ultra high-precision localization. This paper investigates secure communication in cooperative THz orthogonal frequency-division multiplexing (OFDM) bistatic integrated sensing and communications (ISAC) systems, where multiple base stations (BSs) equipped with extremely large-scale antenna arrays (ELAAs) collaboratively serve downlink users while concurrently locating multiple targets. Malicious targets are assumed to act as potential eavesdroppers attempting to intercept confidential information intended for legitimate users. To mitigate these threats, we formulate a joint optimization problem for analog beamforming, digital precoding, true-time delayers (TTDs), and sensing signal covariance matrix design. The objective is to maximize the minimum secrecy rate subject to Cramer-Rao bound (CRB) constraints that ensure localization accuracy. This problem is highly challenging due to the non-convex CRB constraint, strongly coupled variables, high computational complexity from ELAA, and near-field channel modeling. To address these challenges, we propose a novel data-driven framework that integrates graph neural networks (GNNs) with the Mamba architecture. Our proposed framework first encodes the interactions among users, targets, and BSs into a heterogeneous graph and then employs message passing to optimize vertex features. The Mamba blocks further enhance this process through their selection mechanism and state space modeling capabilities, enabling dynamic and context-aware optimization of beamforming, TTD configurations, and sensing parameters. Numerical simulations validate that the proposed method outperforms both conventional and learning-based baselines, while offering high computational efficiency and strong generalization across different network conditions.

| Subjects: | Information Theory (cs.IT) |
| --- | --- |
| Cite as: | [arXiv:2608.10467](https://arxiv.org/abs/2608.10467) \[cs.IT\] |
|  | (or [arXiv:2608.10467v1](https://arxiv.org/abs/2608.10467v1) \[cs.IT\] for this version) |
|  | [https://doi.org/10.48550/arXiv.2608.10467](https://doi.org/10.48550/arXiv.2608.10467) |

## Submission history

From: Chao Wang \[[view email](https://arxiv.org/show-email/f4e98f53/2608.10467)\]  
**\[v1\]** Tue, 11 Aug 2026 04:25:11 UTC (5,058 KB)

[Which authors of this paper are endorsers?](https://arxiv.org/auth/show-endorsers/2608.10467) | Disable MathJax ([What is MathJax?](https://info.arxiv.org/help/mathjax.html))
