Secure Cooperative THz ISAC via Mamba Empowered Graph Neural Network Precoding

2026-08-11

AI Summary

This work addresses the dual challenges of eavesdropping by malicious targets and high-precision localization in cooperative terahertz OFDM bistatic integrated sensing and communication systems. To this end, the authors propose a novel joint optimization framework that simultaneously designs analog beamforming, digital precoding, true time delays, and the covariance matrix of sensing signals. The approach innovatively integrates the Mamba state space model with graph neural networks, leveraging heterogeneous graph representations to capture interactions among users, targets, and base stations, while exploiting Mamba’s dynamic selection mechanism for context-aware optimization. By incorporating near-field channel modeling and true-time-delay beamforming, the proposed method significantly outperforms existing baselines in terms of secrecy rate, computational efficiency, and generalization capability, effectively achieving a balance between secure communication and high-accuracy sensing.

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.

Problem

• THz ISAC
• secure communication
• eavesdropping targets
• localization accuracy
• secrecy rate

Innovation

• Mamba
• Graph Neural Network
• THz ISAC
• Secure Precoding
• Cramer-Rao Bound