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arXiv 2601.19366eess.SP
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协作双智能反射面辅助的 secure 通信 for MIMO-OFDM 系统

Cooperative Double IRS aided Secure Communication for MIMO-OFDM Systems

University of Electronic Science and Technology of China(电子科技大学);Laboratory of Electromagnetic Space Cognition and Intelligent Control(电磁空间认知与智能控制实验室);Tianfu Jiangxi Laboratory(天府江西实验室);National Mobile Communications Research Laboratory, Southeast University(东南大学国家移动通信重点实验室)

Weijie Xiong, Jingran Lin, Di Jiang, Yuhan Zhang, Kai Zhong, Qiang Li

AI总结:

本文提出了一种协作双智能反射面辅助的宽带 MIMO-OFDM 通信方案,通过 manifold 基础方法解决 OFDM 耦合问题,实现显著的 secrecy 率增益。

摘要:

协作双智能反射面(双-IRS)已逐渐成为增强物理层安全(PLS)在 MIMO 系统中的有前途的方法。然而,现有研究局限于窄带场景,并未能解决宽带 MIMO-OFDM。在该场景中,频率平坦 IRS 阶段和级联 IRS 链路导致严重的耦合,使窄带设计不适用。为克服这一挑战,我们引入了协作双-IRS 辅助宽带 MIMO-OFDM,并提出了一种高效的 manifold 基础解决方案。通过将功率和常模约束视为黎曼流形,我们将非凸 secrecy 总速率最大化重新公式化为一个在产品流形上的无约束优化问题。基于此公式,我们进一步开发了产品黎曼梯度下降(PRGD)算法,具有保证的 stationary 收敛性。仿真结果表明,所提出的方案有效解决了 OFDM 耦合问题,并实现了显著的 secrecy 率增益,分别优于单-IRS 和分布式多-IRS 基准 32.0% 和 22.3%。

英文摘要:

Cooperative double intelligent reflecting surface (double-IRS) has emerged as a promising approach for enhancing physical layer security (PLS) in MIMO systems. However, existing studies are limited to narrowband scenarios and fail to address wideband MIMO-OFDM. In this regime, frequency-flat IRS phases and cascaded IRS links cause severe coupling, rendering narrowband designs inapplicable. To overcome this challenge, we introduce cooperative double-IRS-assisted wideband MIMO-OFDM and propose an efficient manifold-based solution. By regarding the power and constant modulus constraints as Riemannian manifolds, we reformulate the non-convex secrecy sum rate maximization as an unconstrained optimization on a product manifold. Building on this formulation, we further develop a product Riemannian gradient descent (PRGD) algorithm with guaranteed stationary convergence. Simulation results demonstrate that the proposed scheme effectively resolves the OFDM coupling issue and achieves significant secrecy rate gains, outperforming single-IRS and distributed multi-IRS benchmarks by 32.0% and 22.3%, respectively.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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