近场ISAC多用户安全通信波束设计

邓志祥; 张志威

doi:10.11999/JEIT250462

近场ISAC多用户安全通信波束设计

doi: 10.11999/JEIT250462 cstr: 32379.14.JEIT250462

邓志祥^{1, 2, ,},
张志威²

1.
河海大学海上智能网信技术教育部重点实验室常州 213251
2.
河海大学信息科学与工程学院常州 213251

基金项目: 河南省重点研发专项项目(251111220700)

详细信息

作者简介:
邓志祥：男，副教授，研究方向为无线物理层安全

张志威：男，硕士生，研究方向为无线物理层安全

通讯作者:
邓志祥　dengzhixiang@hhu.edu.cn

中图分类号: TN918.91
计量
- 文章访问数: 25
- HTML全文浏览量: 9
- PDF下载量: 3
- 被引次数: 0
出版历程
- 收稿日期: 2025-05-27
- 修回日期: 2025-09-18
- 网络出版日期: 2025-09-24

Secure Beamforming Design for Multi-User Near-Field ISAC Systems

DENG Zhixiang^{1, 2
, ,},
ZHANG Zhiwei²

1.
Key Laboratory of Maritime Intelligent Cyberspace Technology (Hohai University), Ministry of Education, Changzhou 213251, China
2.
College of Information Science and Engineering, Hohai University, Changzhou 213251, China

Funds: Key Research and Development Special Project of Henan Province (251111220700)

摘要

摘要: 该文研究了近场通感一体化系统(ISAC)中多用户安全波束设计问题，其中多个单天线通信用户和一个雷达感知目标都位于发射机的近场区域内，雷达目标作为潜在窃听者，可能从联合波束中获取通信信息。为保证系统通信安全性和感知精度，该文以多用户可达安全和速率最大化为目标、以基站发射功率和感知性能为约束条件，构建了通信信号与雷达感知信号波束形成向量的联合优化模型。其中，雷达感知信号间兼具双重功能：一方面作为人工噪声，干扰窃听者对合法通信用户信息的解码；另一方面用于实现对目标的感知，其感知性能通过克拉美罗界(CRB)进行量化。为解决该多变量的非凸优化问题，该文提出了基于半正定松弛(SDR)和加权最小均方误差(WMMSE)的优化算法求解该优化问题。仿真结果表明近场模型所提供的距离自由度，以及引入人工噪声信号，能够为多用户ISAC通信安全带来性能增益。
- 近场通信 /
- 通感一体化 /
- 物理层安全 /
- 加权最小均方误差
Abstract: Objective Integrated Sensing and Communication (ISAC) systems, a key enabling technology for 6G, achieve the joint realization of communication and sensing by sharing spectrum and hardware. However, radar targets may threaten the confidentiality of user communications, necessitating secure transmission against potential eavesdropping. At the same time, large-scale antenna arrays and high-frequency bands are expected to be widely deployed to meet future performance requirements, making near-field wireless transmission increasingly common. This trend creates a mismatch between existing ISAC designs that rely on the far-field assumption and the characteristics of real propagation environments. In this study, we design optimal secrecy beamforming for a multi-user near-field ISAC system to improve the confidentiality of user communications while ensuring radar sensing performance. The results show that distance degrees of freedom inherent in the near-field model, together with radar sensing signals serving as Artificial Noise (AN), provide significant gains in communication secrecy. Methods A near-field ISAC system model is established, in which multiple communication users and a single target, regarded as a potential eavesdropper, are located within the near-field region of a transmitter equipped with a Uniform Linear Array (ULA). Based on near-field channel theory, channel models are derived for all links, including the communication channels from the transmitter to the users, the transmitter to the target, and the radar echo-based sensing channel.The secrecy performance of each user is quantified as the difference between the achievable communication rate and the eavesdropping rate at the target, and the sum secrecy rate across all users is adopted as the metric for system-wide confidentiality. The sensing performance of the ISAC system is evaluated using the Cramér–Rao bound (CRB), obtained from the Fisher Information Matrix (FIM) for parameter estimation. To enhance secrecy, a joint optimization problem is formulated for the beamforming vectors of communication and radar sensing signals, with the objective of maximizing the sum secrecy rate under base station transmit power and sensing performance constraints.As the joint optimization problem is inherently non-convex, an algorithm combining Semi-Definite Relaxation (SDR) and Weighted Minimum Mean Square Error (WMMSE) is developed. The equivalence between the MMSE-transformed problem and the original secrecy rate maximization problem is first established to handle non-convexity. The CRB constraint is then expressed in convex form using the Schur complement. Finally, SDR is applied to recast the problem into a convex optimization framework, which allows a globally optimal solution to be derived. Results and Discussions Numerical evaluations show that the proposed near-field ISAC secrecy beamforming design achieves clear advantages in communication confidentiality compared with far-field and non-AN schemes. Under the near-field channel model, the designed beams effectively concentrate energy on legitimate users while suppressing information leakage through radar sensing signals (Fig. 3b). Even when communication users and radar targets are angularly aligned, the secure beamforming scheme attains spatial isolation through distance-domain degrees of freedom, thereby maintaining positive secrecy rates (Fig. 3a).Joint optimization of communication beams and radar sensing signals significantly improves multi-user secrecy rates while satisfying the CRB constraint. Compared with conventional AN-assisted methods, the proposed solution exhibits superior trade-off performance between sensing and communication (Fig. 4).The number of antennas is directly correlated with beam focusing performance: increasing the antenna count produces more concentrated beam patterns. In the near-field model, however, the incorporation of the distance dimension amplifies this effect, yielding larger performance gains than those observed in conventional far-field systems (Fig. 5).Raising the transmit power further improves the received signal quality at the users, which proportionally enhances system secrecy. The near-field scheme achieves more substantial gains than far-field baselines under higher transmit power conditions (Fig. 6).This paper also examines the effect of user population on secrecy performance. A larger number of users increases inter-user interference, which degrades overall secrecy (Fig. 7). Nevertheless, owing to the intrinsic interference suppression capability of the near-field scheme and the ability of AN to impair eavesdroppers’ decoding, the proposed method maintains stronger robustness against multi-user interference compared with conventional approaches. Conclusions This study investigates multi-user secure communication design in near-field ISAC systems and proposes a beamforming optimization scheme that jointly enhances sensing accuracy and communication secrecy. A non-convex optimization model is established to maximize the multi-user secrecy sum rate under base station transmit power and CRB constraints, where radar sensing signals are exploited as AN to impair potential eavesdroppers. To address the complexity of the problem, a joint optimization algorithm combining SDR and WMMSE is developed, which reformulates the original non-convex problem into a convex form solvable with standard optimization tools.
- Near-Field Communication /
- Integrated Sensing and Communication (ISAC) /
- Physical Layer Security /
- Weighted Minimum Mean Square Error (WMMSE)

HTML全文

图 1 系统模型

下载: 全尺寸图片幻灯片

图 2 算法迭代收敛

下载: 全尺寸图片幻灯片

图 3 通信与雷达信号的归一化信号功率

下载: 全尺寸图片幻灯片

图 4 安全和速率与CRB阈值关系

下载: 全尺寸图片幻灯片

图 5 安全和速率与不同天线数关系

下载: 全尺寸图片幻灯片

图 6 安全和速率与发射功率关系

下载: 全尺寸图片幻灯片

图 7 安全和速率与用户数量关系

下载: 全尺寸图片幻灯片

1 基于SDR和WMMSE的联合优化算法

输入：$ {\boldsymbol{H}}{,}{{\beta }_{e}}{,}a({r_e}{,}{\theta _e}){,\sigma }_{k}^{2}{,\sigma }_{e}^{2}{,\gamma ,}{\text{Pt}}{,\varepsilon ,\eta ,}{{\mathrm{ite}}}{{{\mathrm{r}}}_{{\max}}} $
输出：$\forall {{\boldsymbol{f}}_{k}}{,}{{\boldsymbol{R}}_{\boldsymbol{s}}}$
(1) 初始化
(2) 初始化${{\boldsymbol{f}}_{k}}$使得${\text{tr}}\left(\displaystyle\sum\nolimits_{i = 1}^K {{{\boldsymbol{F}}_i}} \right) = {P_t}$，${\text{iter}} = 1$
(3) 重复
(4) $w_k^{} = \dfrac{{{\boldsymbol{h}}_k^{\text{H}}{{\boldsymbol{f}}_{k}}}}{{\displaystyle\sum\limits_{j = 1}^K \| {\boldsymbol{h}}_k^{\text{H}}{{\boldsymbol{f}}_j}{\|^2} + {\boldsymbol{h}}_k^{\text{H}}{{\boldsymbol{R}}_{\boldsymbol{s}}}{{\boldsymbol{h}}_k} + \sigma _k^2}}$
(5) $b_k^{} = {e_k}^{ - 1}$
(6) 解凸优化(P5)计算${{\boldsymbol{T}}_{k}}$和${{\boldsymbol{R}}_{\boldsymbol{s}}}$
(7) 通过高斯随机化或者特征值分解求${{\boldsymbol{f}}_{k}}$
(8) ${\text{iter}} = {\text{iter}} + 1$
(9) 直到$ \left\| {\displaystyle\sum\nolimits_{i = 1}^K {\left( {{\text{SR}}_i^{(t)} - {\text{SR}}_i^{(t - 1)}} \right)} } \right\| \le \eta $或${\text{iter}} = {\text{ite}}{{\text{r}}_{{\text{max}}}}$

下载: 导出CSV

参考文献(23)

[1]	LU Shihang, LIU Fan, LI Yunxin, et al. Integrated sensing and communications: Recent advances and ten open challenges[J]. IEEE Internet of Things Journal, 2024, 11(11): 19094–19120. doi: 10.1109/JIOT.2024.3361173.
[2]	CONG Jiayi, YOU Changsheng, LI Jiapeng, et al. Near-field integrated sensing and communication: Opportunities and challenges[J]. IEEE Wireless Communications, 2024, 31(6): 162–169. doi: 10.1109/MWC.002.2400033.
[3]	WEI Zhongxiang, LIU Fan, MASOUROS C, et al. Toward multi-functional 6G wireless networks: Integrating sensing, communication, and security[J]. IEEE Communications Magazine, 2022, 60(4): 65–71. doi: 10.1109/MCOM.002.2100972.
[4]	LIAO Weicheng, CHANG T H, MA W K, et al. QoS-based transmit beamforming in the presence of eavesdroppers: An optimized artificial-noise-aided approach[J]. IEEE Transactions on Signal Processing, 2011, 59(3): 1202–1216. doi: 10.1109/TSP.2010.2094610.
[5]	DELIGIANNIS A, DANIYAN A, LAMBOTHARAN S, et al. Secrecy rate optimizations for MIMO communication radar[J]. IEEE Transactions on Aerospace and Electronic Systems, 2018, 54(5): 2481–2492. doi: 10.1109/TAES.2018.2820370.
[6]	LIU Liang, ZHANG Rui, and CHUA K C. Secrecy wireless information and power transfer with MISO beamforming[J]. IEEE Transactions on Signal Processing, 2014, 62(7): 1850–1863. doi: 10.1109/TSP.2014.2303422.
[7]	YU Xianghao, XU Dongfang, SUN Ying, et al. Robust and secure wireless communications via intelligent reflecting surfaces[J]. IEEE Journal on Selected Areas in Communications, 2020, 38(11): 2637–2652. doi: 10.1109/JSAC.2020.3007043.
[8]	NEGI R and GOEL S. Secret communication using artificial noise[C]. 2005 IEEE 62nd Vehicular Technology Conference, Dallas, USA, 2005: 1906–1910. doi: 10.1109/vetecf.2005.1558439.
[9]	GOEL S and NEGI R. Guaranteeing secrecy using artificial noise[J]. IEEE Transactions on Wireless Communications, 2008, 7(6): 2180–2189. doi: 10.1109/twc.2008.060848.
[10]	MA Hui, CHENG Julian, WANG Xianfu, et al. Robust MISO beamforming with cooperative jamming for secure transmission from perspectives of QoS and secrecy rate[J]. IEEE Transactions on Communications, 2018, 66(2): 767–780. doi: 10.1109/TCOMM.2017.2765637.
[11]	LIN Zhi, LIN Min, OUYANG Jian, et al. Robust secure beamforming for multibeam satellite communication systems[J]. IEEE Transactions on Vehicular Technology, 2019, 68(6): 6202–6206. doi: 10.1109/tvt.2019.2913793.
[12]	DE LIMA C, BELOT D, BERKVENS R, et al. Convergent communication, sensing and localization in 6G systems: An overview of technologies, opportunities and challenges[J]. IEEE Access, 2021, 9: 26902–26925. doi: 10.1109/access.2021.3053486.
[13]	REN Zixiang, QIU Ling, and XU Jie. Optimal transmit beamforming for secrecy integrated sensing and communication[C]. ICC 2022-IEEE International Conference on Communications, Seoul, Korea, Republic of, 2022: 5555–5560. doi: 10.1109/ICC45855.2022.9838753.
[14]	WANG Xin, FEI Zuo, and HUANG Jing. Near-field ISAC for multi-target detection and communication[J]. IEEE Journal on Selected Areas in Communications, 2023, 41(6): 1723–1735. doi: 10.1109/JSAC.2023.3267890.(查阅网上资料,未找到本条文献信息,请确认).
[15]	WANG Yiqiu, TAO Meixia, and SUN Shu. Cramér-Rao bound analysis and beamforming design for integrated sensing and communication with extended targets[J]. IEEE Transactions on Wireless Communications, 2024, 23(11): 15987–16000. doi: 10.1109/twc.2024.3435864.
[16]	WANG Zhaolin, MU Xidong, and LIU Yuanwei. Near-field integrated sensing and communications[J]. IEEE Communications Letters, 2023, 27(8): 2048–2052. doi: 10.1109/lcomm.2023.3280132.
[17]	SELVAN K T and JANASWAMY R. Fraunhofer and Fresnel distances: Unified derivation for aperture antennas[J]. IEEE Antennas and Propagation Magazine, 2017, 59(4): 12–15. doi: 10.1109/map.2017.2706648.
[18]	BJÖRNSON E, DEMIR Ö T, and SANGUINETTI L. A primer on near-field beamforming for arrays and reconfigurable intelligent surfaces[C]. 2021 55th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, USA, 2021: 105–112. doi: 10.1109/IEEECONF53345.2021.9723331.
[19]	SUN Ping, DAI Haibo, and WANG Baoyun. Integrated sensing and secure communication with XL-MIMO[J]. Sensors, 2024, 24(1): 295. doi: 10.3390/s24010295.
[20]	YANG Ruiwei and DU Huiqin. Joint precoding and artificial noise design for secure transmission in ISAC system[C]. 2023 IEEE 24th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Shanghai, China, 2023: 16–20. doi: 10.1109/SPAWC53906.2023.10304462.
[21]	SHI Qingjiang, RAZAVIYAYN M, LUO Zhiquan, et al. An iteratively weighted MMSE approach to distributed sum-utility maximization for a MIMO interfering broadcast channel[J]. IEEE Transactions on Signal Processing, 2011, 59(9): 4331–4340. doi: 10.1109/TSP.2011.2147784.
[22]	CHRISTENSEN S S, AGARWAL R, DE CARVALHO E, et al. Weighted sum-rate maximization using weighted MMSE for MIMO-BC beamforming design[J]. IEEE Transactions on Wireless Communications, 2008, 7(12): 4792–4799. doi: 10.1109/t-wc.2008.070851.
[23]	LUO Zhiquan, MA W K, SO A M C, et al. Semidefinite relaxation of quadratic optimization problems[J]. IEEE Signal Processing Magazine, 2010, 27(3): 20–34. doi: 10.1109/msp.2010.936019.