多无人机信干噪比适配的符号级预编码与位置联合抗干扰优化策略

魏浩然; 姚如贵; 樊晔; 马为鑫; 左晓亚

doi:10.11999/JEIT250221

多无人机信干噪比适配的符号级预编码与位置联合抗干扰优化策略

doi: 10.11999/JEIT250221 cstr: 32379.14.JEIT250221

魏浩然¹,
姚如贵¹,
樊晔^{1, 2, ,},
马为鑫¹,
左晓亚¹

1.
西北工业大学电子信息学院西安 710129
2.
西北工业大学深圳研究院深圳 518057

基金项目: 国家自然科学基金(62401473)，空间微波通信国家重点实验室基金(HTKJ2024KL504010)，深圳市科技计划资助项目(JCYJ20240813150735045)，陕西省重点研发计划一般项目(2025CY-YBXM-055)，西北工业大学无人飞行器技术全国重点实验室开放课题(WR202404)，中央高校基本科研业务费专项资金(G2024WD0159, D5000240239)

详细信息

作者简介:
魏浩然：男，硕士研究生，研究方向为干扰通信一体化

姚如贵：男，教授，研究方向为物理层安全、卫星通信、通信感知一体化等

樊晔：女，副教授，研究方向为无线安全通信、干扰管理技术等

马为鑫：男，硕士生，研究方向为多无人机轨迹优化

左晓亚：男，副教授，研究方向为宽带无线通信技术、高速数据链技术、MIMO技术等

通讯作者:
樊晔　fanye@nwpu.edu.cn

中图分类号: TN929.5
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出版历程
- 收稿日期: 2025-04-01
- 修回日期: 2025-07-02
- 网络出版日期: 2025-07-07

SINR Adaptive Symbol Level Precoding and Position Joint Optimization Strategy for Multiple Unmanned Aerial Vehicles Anti-Jamming Communication

WEI Haoran¹,
YAO Rugui¹,
FAN Ye^{1, 2
, ,},
MA Weixin¹,
ZUO Xiaoya¹

1.
College of Electronic Information, Northwestern Polytechnical University, Xi’an 710129, China
2.
Northwestern Polytechnical University Shenzhen Research Institute, Shenzhen 518087, China

Funds: The National Natural Science Foundation of China (62401473). The National Key Laboratory Fund Project for Space Microwave Communication (HTKJ2024KL504010). Shenzhen Science and Technology Program (JCYJ20240813150735045). The Key R&D Project in Shaanxi Province (2025CY-YBXM-055). The National Key Laboratory of Unmanned Aerial Vehicle Technology in NPU(WR202404), The Fundamental Research Funds for the Central Universities (G2024WD0159, D5000240239)

摘要

摘要: 无人机部署为空中通信节点可为地面用户提供更为灵活、更高质量的服务。然而，无人机易受到外界恶意干扰导致通信性能严重下降。传统抗干扰方案如跳频抗干扰计算量大，在计算资源与能量受限的无人机上难以适用。针对上述问题，该文综合采用多点协作(CoMP)技术和符号级预编码(SLP)技术，提出多无人机信干噪比适配的符号级预编码与位置联合抗干扰优化策略。针对三维空间和存在同一节点属于多个集合等挑战，巧妙地设计了3D-Kmeans++协作集划分的空地多节点匹配机制。为了解决预编码矩阵和位置联合优化时计算量大的问题，基于最优预编码矩阵和位置之间的关联关系，提出基于粒子重构的低复杂度迭代优化算法，可同时求解出无人机的预编码矩阵和位置。另外，针对用户所受干扰和噪声的差异化功率特征，提出信干噪比(SINR)适配的符号级预编码，实现了多架无人机功率分配的优化设计。仿真结果表明，与不划分协作集对比，采用该文所提空地多节点匹配机制后受干扰通信用户的信干噪比提升5 dB左右；与传统符号级预编码对比，采用该文所提预编码和迭代优化算法，受干扰通信用户的信干噪比能提升12～13 dB，同时保证普通通信用户的正常通信不受影响，提升了系统能量效率，降低了计算复杂度。
- 无人机部署 /
- 3D-Kmeans++协作集划分 /
- 粒子重构 /
- 信干噪比适配的符号级预编码
Abstract: Objective Unmanned Aerial Vehicles (UAVs) are widely applied in areas such as traffic monitoring, wireless coverage, and precision agriculture due to their high mobility and deployment flexibility. In air–ground communication scenarios requiring flexible deployment, UAV mobility can be leveraged to counteract external malicious jamming. Further, the collaborative operation of multiple UAVs enables improved system performance. However, the broadcast nature of wireless communication renders multiple UAV communication systems vulnerable to jamming attacks that disrupt legitimate communication services. Addressing this challenge, this study proposes a Signal-to-Interference-plus-Noise Ratio (SINR) adaptive Symbol-Level Precoding (SLP) and position joint optimization strategy for anti-jamming communication in multi-UAV systems. The approach fully exploits UAV mobility to enhance communication robustness under different user requirements. By integrating Coordinated Multi-Point (CoMP) transmission with SLP in a Multi-User Multiple-Input Single-Output (MU-MISO) system, the strategy improves interference utilization, enhances system energy efficiency, and reduces computational complexity. Methods An SINR adaptive SLP and position joint anti-jamming optimization strategy for multiple UAVs is proposed by integrating CoMP and SLP technologies. To address the challenges of three-dimensional operational space and the overlap of nodes assigned to multiple sets, a ground-to-air multi-node matching mechanism based on three-dimensional K-means++ collaborative set partitioning is designed. To reduce the computational complexity of the joint optimization process, an iterative optimization algorithm based on particle reconstruction is developed. This algorithm simultaneously solves for both the UAV precoding matrix and spatial positions with low computational overhead. Additionally, an SINR adaptive SLP approach is introduced to enable optimized power allocation for multiple UAVs, considering the varying power characteristics of jamming and noise experienced by users. Simulation results demonstrate that the integration of CoMP and SLP technologies effectively enhances the communication performance of jammed users, while stable communication performance is maintained for ordinary users. Results and Discussions In the proposed UAV anti-jamming communication strategy, the SINR of jammed users is improved without compromising the normal communication performance of ordinary users. In the simulation results, UAV positions are marked with five-pointed stars (Figs. 6 and 7), UAV coverage areas are represented by circles, and users are indicated by “*”. A comparison of SINR variations under four schemes (Fig. 8) shows that the received SINR of jammed users increases by approximately 12～13 dB, while the SINR of ordinary users remains above the required threshold. When different SINR thresholds are applied, the received SINR of each user type varies accordingly (Fig. 9). By setting appropriate thresholds based on actual scenario requirements, different energy allocation effects can be achieved. Following optimization, the Bit Error Rate (BER) of jammed users is significantly reduced (Fig. 10). The constellation diagrams comparing the received signals under two precoding schemes (Fig. 11) indicate that the proposed SINR adaptive SLP strategy for multiple UAVs effectively improves the SINR of jammed users, while maintaining the communication quality of ordinary users. Moreover, the fitness evolution curve of the iterative optimization algorithm based on particle reconstruction (Fig. 12) shows that the algorithm approaches the global optimal solution at an early stage of iteration. Conclusions To address the challenge of anti-jamming communication for multi-user services supported by multiple UAVs, this study integrates CoMP transmission with SLP technology and proposes an SINR adaptive SLP and position joint optimization strategy for multi-UAV anti-jamming communication. The strategy is implemented in two stages. First, to solve the clustering problem in three-dimensional space and allow nodes to belong to multiple groups, a ground-to-air multi-node matching mechanism based on three-dimensional K-means++ collaborative set partitioning is designed. Second, an SINR adaptive SLP method is proposed to optimize UAV power allocation based on the SINR requirements of different user types. To reduce the computational complexity of jointly optimizing the precoding matrix and UAV positions, an iterative optimization algorithm based on particle reconstruction is developed. Simulation results demonstrate that the proposed strategy, by combining CoMP and SLP, effectively improves the communication performance of jammed users while maintaining reliable communication for ordinary users.

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图 1 系统模型

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图 2 多无人机信干噪比适配的符号级预编码与位置联合抗干扰优化策略流程图

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图 3 基于3D-Kmeans++协作集划分的空地多节点匹配机制流程图

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图 4 符号级预编码有益干扰区域

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图 5 基于粒子重构的迭代优化算法流程图

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图 6 联合算法前无人机位置图

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图 7 联合算法后无人机位置图

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图 8 不同方案对应用户信干噪比变化图

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图 9 信干噪比阈值变化对比图

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图 10 受干扰通信用户误码率随干扰功率变化图

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图 11 接收信号星座点对比图

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图 12 基于粒子重构的迭代优化算法的适应度进化曲线

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表 1 仿真参数

名称	符号	数值
用户数量	$ N $	10
无人机数量	$ M $	3
协作无人机数量	$ {N_x} $	2
无人机发送信号总功率(W)	$ P $	1
无人机飞行高度范围(m)	$ \left[ {{H_{\min }},{H_{\max }}} \right] $	[400, 600]
无人机飞行水平范围(m)	$ \left[ {{x_{\lim }},{y_{\lim }}} \right] $	[250, 250]
无人机覆盖半径与高度的比值	$ \tan \theta /2 $	1
信干噪比阈值(dB)	$ {{\mathrm{SINR}}_\gamma } $	3
高斯白噪声功率(W)	$ {N_0} $	$ {10^{ - 10}} $

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参考文献(18)

[1]	何江, 喻莞芯, 黄浩, 等. 多无人机分布式感知任务分配-通信基站关联与飞行策略联合优化设计[J]. 电子与信息学报, 2025, 47(5): 1402–1417. doi: 10.11999/JEIT240738. HE Jiang, YU Wanxin, HUANG Hao, et al. Joint task allocation, communication base station association and flight strategy optimization design for distributed sensing unmanned aerial vehicles[J]. Journal of Electronics & Information Technology, 2025, 47(5): 1402–1417. doi: 10.11999/JEIT240738.
[2]	孙伟皓, 王海, 秦蓁, 等. 机会无人机辅助数据收集的组网和资源分配方法[J]. 电子与信息学报, 2025, 47(5): 1381–1391. doi: 10.11999/JEIT241053. SUN Weihao, WANG Hai, QIN Zhen, et al. Networking and resource allocation methods for opportunistic UAV-assisted data collection[J]. Journal of Electronics & Information Technology, 2025, 47(5): 1381–1391. doi: 10.11999/JEIT241053.
[3]	CHENG Sixi, LING Xiang, and ZHU Lidong. Deep reinforcement learning-based anti-jamming approach for fast frequency hopping systems[J]. IEEE Open Journal of the Communications Society, 2025, 6: 961–971. doi: 10.1109/OJCOMS.
[4]	LI Wengang, ZU Jiahao, HUANG Jun, et al. Hybrid beamforming for distributed communication systems in internet of things[C]. 2024 IEEE International Conference on Communications Workshops (ICC Workshops), Denver, USA, 2024: 1469–1474. doi: 10.1109/ICCWorkshops59551.2024.10615566.
[5]	BERRA S, ALBREEM M A M, and ABED M S. A low complexity linear precoding method for massive MIMO[C]. 2020 International Conference on UK-China Emerging Technologies (UCET), Glasgow, UK, 2020: 1–4. doi: 10.1109/UCET51115.2020.9205420.
[6]	YANG Hong and MARZETTA T L. Performance of conjugate and zero-forcing beamforming in large-scale antenna systems[J]. IEEE Journal on Selected Areas in Communications, 2013, 31(2): 172–179. doi: 10.1109/JSACC.2013.130206.
[7]	FAN Kuangang, YANG Chunrong, and TAN Peiao. A new transmission scheme: Semi-blind channel estimation combined with precoding[C]. The 13th International Conference on Communications, Circuits and Systems (ICCCAS), Xiamen, China, 2024: 391–396. doi: 10.1109/ICCCAS62034.2024.10652733.
[8]	WIESEL A, ELDAR Y C, and SHAMAI S. Zero-forcing precoding and generalized inverses[J]. IEEE Transactions on Signal Processing, 2008, 56(9): 4409–4418. doi: 10.1109/TSP.2008.924638.
[9]	SADEK M, TARIGHAT A, and SAYED A H. A leakage-based precoding scheme for downlink multi-user MIMO channels[J]. IEEE transactions on Wireless Communications, 2007, 6(5): 1711–1721. doi: 10.1109/TWC.2007.360373.
[10]	LI Ang, SPANO D, KRIVOCHIZA J, et al. A tutorial on interference exploitation via symbol-level precoding: Overview, state-of-the-art and future directions[J]. IEEE Communications Surveys & Tutorials, 2020, 22(2): 796–839. doi: 10.1109/COMST.2020.2980570.
[11]	WANG Yu, SUN Chen, and GAO Xiqi. Symbol error probability minimization for symbol-level precoding in massive MIMO communications[C]. 2024 IEEE Wireless Communications and Networking Conference (WCNC), Dubai, United Arab Emirates, 2024: 1–6. doi: 10.1109/WCNC57260.2024.10571339.
[12]	WANG Yafei, HOU Hongwei, WANG Wenjin, et al. Symbol-level precoding for average SER minimization in multiuser MISO systems[J]. IEEE Wireless Communications Letters, 2024, 13(4): 1103–1107. doi: 10.1109/LWC.2024.3361487.
[13]	LAI C C, TSAI A H, TING C W, et al. Interference-aware deployment for maximizing user satisfaction in multi-UAV wireless networks[J]. IEEE Wireless Communications Letters, 2023, 12(7): 1189–1193. doi: 10.1109/LWC.2023.3266011.
[14]	DEMIR U, TOKER C, and EKICI Ö. Multi-UAV base station deployment maximizing coverage density and network endurance[C]. 2020 28th Signal Processing and Communications Applications Conference (SIU), Gaziantep, Turkey, 2020: 1–4. doi: 10.1109/SIU49456.2020.9302046.
[15]	NUANYAI K, TARBUT P, and CHANTARASKUL S. Clustering interval effects on dynamic CoMP clustering in ultra-dense networks[C]. 2024 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics (RI2C), Bangkok, Thailand, 2024: 17–23. doi: 10.1109/RI2C64012.2024.10784428.
[16]	TIAN Wen, DING Xufei, LIU Guangjie, et al. A UAV-assisted secure communication system by jointly optimizing transmit power and trajectory in the Internet of Things[J]. IEEE Transactions on Green Communications and Networking, 2023, 7(4): 2025–2037. doi: 10.1109/TGCN.2023.3235887.
[17]	TANG Na, TANG Hongying, LI Baoqing, et al. Joint maneuver and beamwidth optimization for UAV-enabled multicasting[J]. IEEE Access, 2019, 7: 149503–149514. doi: 10.1109/ACCESS.2019.2947031.
[18]	LIU Tao. Research on naive Bayes integration method based on Kmeans++ digital teaching clustering[C]. The 8th International Conference on Information Systems Engineering (ICISE), Dalian, China, 2023: 302–305. doi: 10.1109/ICISE60366.2023.00069.