AI 中文总结
该研究针对协作式太赫兹ISAC系统的安全通信问题，提出Mamba赋能GNN预编码框架，优化波束成形等参数，提升保密速率与计算效率，性能优于传统及学习型方法。
AI 中文摘要
太赫兹（THz）频段为高吞吐量通信和超高精度定位提供了丰富的频谱资源。本文研究协作式太赫兹正交频分复用（OFDM）双基地一体化感知通信（ISAC）系统中的安全通信问题，该系统中多个配备极大规模天线阵列（ELAAs）的基站（BS）协作服务下行用户，同时定位多个目标。恶意目标被视为潜在窃听者，试图拦截 intended 给合法用户的机密信息。为缓解这些威胁，我们构建了模拟波束成形、数字预编码、真时延单元（TTDs）和感知信号协方差矩阵设计的联合优化问题，目标是在确保定位精度的克拉美罗下界（CRB）约束下最大化最小保密速率。该问题因非凸CRB约束、变量强耦合、ELAA带来的高计算复杂度以及近场信道建模而极具挑战性。为应对这些挑战，我们提出了一种将图神经网络（GNN）与Mamba架构相结合的新型数据驱动框架。该框架首先将用户、目标和基站之间的交互编码为异构图，然后利用消息传递优化顶点特征；Mamba模块通过其选择机制和状态空间建模能力进一步增强这一过程，实现波束成形、TTD配置和感知参数的动态、上下文感知优化。数值仿真验证表明，所提方法优于传统及学习型基线，且在不同网络条件下具有高计算效率和强泛化性。
英文摘要
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.
