智能超表面赋能的D2D隐蔽通信策略研究

吕璐; 郑彭玮; 杨龙; 陈健

doi:10.11999/JEIT250045

智能超表面赋能的D2D隐蔽通信策略研究

doi: 10.11999/JEIT250045

西安电子科技大学通信工程学院西安 710000

基金项目: 国家自然科学基金(62271368, 62371367)，陕西省重点研发计划(2023-ZDLGY-50)，中央高校基本科研业务费(QTZX23066)

详细信息

作者简介:
吕璐：男，副教授，研究方向为非正交多址接入、智能超表面和隐蔽通信等

郑彭玮：男，硕士生，研究方向为智能超表面及隐蔽通信

杨龙：男，教授，研究方向为隐蔽通信、无线物理层安全和协作通信等

陈健：男，教授，研究方向为协作通信、智能超表面和隐蔽通信等

通讯作者:
吕璐　lulv@xidian.edu.cn

1¹⁾ 体现了利用RIS实现隐蔽通信的低成本特性：通过引入RIS的相移随机性，无需额外干扰源或不确定性源为监听者功率检测创造不确定性。
2²⁾当监听者可以SIC解码信号时，本文设计方案的性能会受到一定的影响。但本文提出的方案仍能在一定程度上保证安全性：优化RIS相移可以使得合法信道强于窃听信道，易使得监听者的SIC解码失败。监听者对蜂窝用户的信号进行SIC存在一定困难：难以获知蜂窝用户信号的先验信息(如调制方式、编码序列等)；无法获知RIS相移；与蜂窝用户进行同步存在困难。综上所述，本文假设监听者处使用能量检测，无法使用SIC解码具有一定的合理性。³⁾本文信道模型考虑存在直射径以及其他多径分量，故小尺度衰落服从莱斯分布。
3⁴⁾人工噪声干扰方案的复杂度通常为\begin{document}$O(M)^3 $\end{document}，M为噪声源的天线数量，所提方案在算法复杂度基本一致的情况下，隐蔽性能明显优于人工噪声干扰方案。
中图分类号: TN92
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出版历程
- 收稿日期: 2025-01-20
- 修回日期: 2025-04-01
- 网络出版日期: 2025-04-14

Reconfigurable Intelligent Surface-empowered Covert Communication Strategies for D2D Systems

School of Communication Engineering, Xidian University, Xi’an 710000, China

Funds: The National Natural Science Foundation of China (62271368, 62371367); The Key Research and Development Program of Shaanxi (2023-ZDLGY-50); The Fundamental Research Funds for the Central Universities (QTZX23066)

摘要

摘要: 为了应对目前设备到设备 (D2D) 隐蔽通信研究中引入额外不确定性源而带来高成本、高功耗的问题，该文提出一种智能超表面 (RIS) 赋能的D2D隐蔽传输方法。该方法借助RIS为合法用户创造更好的无线传播条件的同时，利用RIS的相移不确定性来混淆监听者的检测。为探究所提方法的隐蔽性能增益，在保证监听者低检测概率以及满足蜂窝用户服务质量的条件下，构建通过联合优化蜂窝用户、D2D发射机的发射功率以及RIS相移来最大化D2D用户的隐蔽速率的优化问题。为解决上述变量和约束高度耦合的非凸优化问题，提出一种高效的基于高斯随机化的交替优化算法，求解出最优的蜂窝用户和D2D发射机的发射功率以及RIS相移。仿真结果表明，RIS的辅助给D2D隐蔽传输系统带来了显著的性能提升，通过增加RIS反射元件数量，或提升蜂窝用户发射功率为D2D隐蔽传输提供更好的掩体都可以进一步提升隐蔽通信性能。
- 智能超表面 /
- D2D网络 /
- 隐蔽通信
Abstract: Objective The rising demand for secure communication in sensitive data transmission scenarios has increased interest in covert communication research. Existing Device-to-Device (D2D) covert communication solutions typically employ additional uncertainty mechanisms, such as artificial noise, leading to elevated energy consumption and implementation complexity. This study addresses these issues by investigating a novel covert communication strategy enabled by Reconfigurable Intelligent Surfaces (RIS). The strategy exploits RIS to enhance wireless propagation for legitimate users and simultaneously introduces controlled phase-shift uncertainty to impair eavesdropping effectiveness. The primary objective is to maximize the covert communication rate among D2D users while maintaining a low probability of detection and guaranteeing the Quality of Service (QoS) requirements for cellular users. Methods The proposed framework consists of an RIS-assisted D2D communication network comprising one cellular user, one pair of D2D users, and an eavesdropper aiming to detect ongoing communications. A comprehensive optimization problem is established to jointly optimize the transmit powers of both the cellular and D2D transmitters, as well as the phase shifts of the RIS, to maximize the covert communication rate for D2D users. Given the non-convex nature and highly interdependent variables within the optimization problem, an alternating optimization algorithm utilizing Gaussian randomization is developed. This algorithm iteratively determines the optimal transmission powers and RIS phase-shift configurations, adhering strictly to constraints on power consumption, RIS characteristics, and covert communication detection probabilities. Additionally, Successive Interference Cancellation (SIC) is integrated at the D2D receiver to effectively mitigate interference from cellular communications, facilitating accurate decoding of covert signals. Results and Discussions Simulation results confirm the efficacy of the proposed RIS-enabled covert communication strategy, showing significant performance enhancements over traditional methods. The inclusion of RIS notably improves the covert communication rate for D2D transmissions. For instance, increasing the number of RIS reflective elements enhances system performance further by introducing greater uncertainty in the received signals at the eavesdropper, thus complicating detection efforts (Fig. 6). Furthermore, it is observed that the cellular user's transmit power inherently acts as an effective shield, increasing confusion for eavesdropping attempts and thus reducing detection accuracy.Convergence of the proposed optimization algorithm is validated through iterative simulation experiments, demonstrating stable and reliable performance across varied conditions and constraints (Fig. 3). Additionally, Monte Carlo simulations verify the accuracy of the analytical expressions derived for the minimum average detection error probability achievable by the eavesdropper, highlighting the critical role of RIS in generating sufficient energy uncertainty to ensure covert communication effectiveness (Fig. 4, Fig. 5). Comparative analyses further illustrate the superior performance of the proposed RIS-based approach relative to conventional artificial noise techniques, particularly in scenarios demanding high covert communication rates. Moreover, the integration of RIS and SIC methods demonstrates notable benefits; SIC efficiently reduces interference from cellular signals, maintaining the cellular user's QoS without compromising the integrity of covert signals decoded at the D2D receiver. Conclusions This study proposes an advanced RIS-empowered covert communication strategy tailored specifically for D2D networks. The approach successfully leverages RIS-induced phase-shift uncertainty and capitalizes on cellular transmissions as natural interference sources, significantly enhancing covert communication capabilities. Through joint optimization of transmission power allocation and RIS configurations, the proposed method effectively maximizes the covert communication rate while satisfying QoS constraints for cellular users. These promising results establish a solid foundation for future exploration into active RIS-assisted communication schemes and the development of sophisticated optimization strategies aimed at further improving covert communication effectiveness.
- Reconfigurable Intelligent Surface (RIS) /
- Device-to-Device (D2D) Communication /
- Covert communication

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图 1 系统模型

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图 2 AO算法处理流程框图

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图 3 RIS辅助D2D传输网络隐蔽通信仿真拓扑结构

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图 4 算法2收敛性曲线

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图 5 最小平均错误检测概率随蜂窝用户发射功率变化曲线

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图 6 最小平均错误检测概率随D2D发射机发射功率变化曲线

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图 7 隐蔽速率随发射功率变化曲线

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图 8 隐蔽速率与RIS反射元件数关系曲线

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图 9 隐蔽速率随隐蔽因子$ \varepsilon $的变化曲线

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1 高斯随机化算法

(1) 如果$ {\text{rank}}(U(t + 1)) = 1 $，则执行：
(2) 　计算$ U(t + 1) $的非零特征值$ {\lambda _{{\mathrm{eigen}}}} $和特征向量$ {\nu _{{\mathrm{eigen}}}} $；
(3) 　返回$ {\boldsymbol{\varTheta}} \left( {t + 1} \right) = {\text{diag}}(\sqrt {{\lambda _{{\mathrm{eigen}}}}} {\nu _{{\mathrm{eigen}}}}) $；
(4) 否则，执行：
(5) 　对于$ q = 1,2,\cdots,Q $：
(6) 　计算$ {\boldsymbol{U}}(t + 1) = {\boldsymbol{V}}\Sigma {{\mathbf{V}}^{\rm H}} $并生成$ {e_q} = {\boldsymbol{V}}{\Sigma ^{\frac{1}{2}}}r $，其中　　　 $ {r_q}\text{～}\mathcal{C}\mathcal{N}\left( {{{\mathbf{0}}_{N + 1}},{{\bf{I}}_{N + 1}}} \right) $；
(7) 　计算：
$ {{\boldsymbol{\varTheta}} _q} = {{\mathrm{diag}}} \left( {{{\rm e}^{{\mathrm{j}}\arg \left( {\frac{{{{\mathbf{e}}_q}[1]}}{{{{\mathbf{e}}_q}[N + 1]}}} \right)}}, \cdots ,{{\rm e}^{j\arg \left( {\frac{{{{\mathbf{e}}_q}[N]}}{{{{\mathbf{e}}_q}[N + 1]}}} \right)}}} \right) $；
(8) 　解决问题(29)，更新其目标值，记为$ {R_q} $；
(9) 　返回：
$ {\mathrm{return}}{\text{ }}{\boldsymbol{\varTheta}} \left( {t + 1} \right){\text{ }} = {\text{ }}{\mathrm{arg}}{\text{ }}{\max_{q = 1,2,\cdots,Q}}{\text{ }}{R_q} $；
(10) 　结束：

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2 基于高斯随机化的交替优化算法

(1)初始化：${\boldsymbol{\varTheta}} \left( 1 \right)$, ${P_{\rm c}}\left( 1 \right)$, ${P_{\rm t}}\left( 1 \right)$, $ \varrho $；设置迭代索引$t = 1$；
(2)重复：
(3) 　设定$ {\boldsymbol{\varTheta}} \left( t \right) $，解决问题(22)，更新$ {P_{\rm c}}\left( {t + 1} \right) $和$ {P_{\rm t}}\left( {t + 1} \right) $；
(4) 　设定$ {P_{\rm c}}\left( {t + 1} \right) $和$ {P_{\rm t}}\left( {t + 1} \right) $，解决问题(28)，更新　　　 $ U(t + 1) $；
(5) 　更新$ t = t + 1 $；
(6)　结束：目标值的变化量低于阈值$ \varrho $；
(7) 　通过算法1从$ U(t + 1) $中恢复$ Q\left( {t + 1} \right) $；

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