面向大语言模型协作感知的海域通信抗干扰

刘欢欢; 肖亮; 林蔚祺; 张朋丽; 陈灏宇; 陈宏毅; 汤中卫

doi:10.12263/DZXB.20250620

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面向大语言模型协作感知的海域通信抗干扰

学术论文 | 更新时间：2026-06-16

- 面向大语言模型协作感知的海域通信抗干扰
- Anti-Jamming Maritime Communications for LLM-Assisted Collaborative Perception
- 电子学报 2026年54卷第3期页码：1252-1262
- 作者机构：
  
  1.厦门大学信息学院，福建厦门 361102
  2.厦门大学人工智能研究院，福建厦门 361105
- 作者简介：
  
  刘欢欢女，1997年10月出生于黑龙江省五常市。现为厦门大学信息学院博士研究生。主要研究方向为无线通信抗干扰等。E-mail: 1046769527@qq.com
  肖亮女，1980年3月出生于河北省石家庄市。现为厦门大学信息学院教授、博士生导师。主要研究方向为无线通信、网络安全等。中国电子学会会员编号：E190014082S。E-mail: lxiao@xmu.edu.cn
  林蔚祺男，2002年9月出生于福建省龙岩市。现为厦门大学信息学院硕士研究生。主要研究方向为无线通信抗干扰。E-mail: 2140199773@qq.com
  张朋丽女，2001年12月出生于河南省周口市。现为厦门大学信息学院博士研究生。主要研究方向为无人机通信抗干扰等。E-mail: 3024975773@qq.com
  陈灏宇男，1998年6月出生于福建省宁德市。现为厦门大学人工智能研究院博士研究生。主要研究方向为物理层安全博弈和无线定位。E-mail: haoyuchen98@qq.com
  陈宏毅男，2001年9月出生于福建省宁德市。现为厦门大学人工智能研究院硕士研究生。主要研究方向为大语言模型协同推断和车联网协作感知技术。E-mail: 314948829@qq.com
  汤中卫男，1999年12月出生于安徽省六安市。现为厦门大学信息学院博士研究生。主要研究方向为计算机视觉与无线通信网络。E-mail: 23320250157889@stu.xmu.edu.cn
- 基金信息：
  
  国家自然科学基金(U25A20388);中央高校基本科研业务费专项资金资助(20720250036);国家重点研发计划(2023YFB3107603)
- DOI：10.12263/DZXB.20250620
  中图分类号： TN92;
- 收稿：2025-07-14，
  
  录用：2026-02-28，
  
  纸质出版：2026-03-25
- 稿件说明：
移动端阅览
刘欢欢, 肖亮, 林蔚祺, 等. 面向大语言模型协作感知的海域通信抗干扰[J]. 电子学报, 2026, 54(03): 1252-1262.

LIU Huanhuan, XIAO Liang, LIN Weiqi, et al. Anti-Jamming Maritime Communications for LLM-Assisted Collaborative Perception[J]. Acta Electronica Sinica, 2026, 54(03): 1252-1262.
刘欢欢, 肖亮, 林蔚祺, 等. 面向大语言模型协作感知的海域通信抗干扰[J]. 电子学报, 2026, 54(03): 1252-1262. DOI：10.12263/DZXB.20250620

LIU Huanhuan, XIAO Liang, LIN Weiqi, et al. Anti-Jamming Maritime Communications for LLM-Assisted Collaborative Perception[J]. Acta Electronica Sinica, 2026, 54(03): 1252-1262. DOI：10.12263/DZXB.20250620

摘要

海域恶劣无线信道和干扰攻击增加了感知数据和反馈信息的传输难度，进而降低了协作感知性能。为支撑基于大语言模型的协作感知和目标识别任务，海域通信需为文本、图像、视频和点云等多模态数据提供高可靠传输，满足多样化服务质量需求。为此，提出面向大语言模型协作感知的海域通信抗干扰技术，除了数据量、信道增益和历史性能外，进一步利用大语言模型根据接收到的多模态数据和文本提示词推断生成的通信环境，以及频谱感知获取的干扰特征，联合优化传输功率、信道和模型选择，在恶劣信道下防御干扰攻击和通信干扰，支撑基于多模态数据的协作感知。针对恶劣海域信道导致的反馈延迟或丢失问题，设计反馈恢复机制，提升多模态数据传输质量。建立海域通信抗干扰博弈模型，刻画海域终端和干扰机间的交互机理，分析纳什均衡点存在条件并给出通感性能界限，揭示模态数量和信道状态等参数对性能的影响。基于WaterScenes数据集和LLaVA等大语言模型的仿真结果表明，在防御基于Q学习的智能干扰机时，所提方案相较于对比方案可提升13.6%的感知精度，降低66.2%的通信能耗和21.7%的时延。

Abstract

Harsh maritime wireless channels and jamming attacks increase the transmission difficulty of sensing data and feedback information

thereby degrading collaborative perception performance. To support large language model (LLM)-assisted collaborative perception and object detection

maritime communications must provide highly reliable transmission for multi-modal data such as text

images

videos and point clouds to meet diverse quality-of-service requirements. In this paper

we propose an anti-jamming maritime communications scheme for LLM-assisted collaborative perception. Besides the data size

channel gain

and historical performance

the communication environment extracted by LLM based on received multi-modal data and prompt

as well as jamming features obtained via spectrum sensing

are further used to optimize the transmit power

channel and LLM selection for transmitting the multi-modal data to support collaborative perception against jamming and interference under harsh channel conditions. A feedback recovery mechanism is designed to address delayed feedback or loss caused by harsh maritime channels and improve the reliability of multi-modal data transmission. The interaction between maritime terminals and the jammer is formulated as the maritime anti-jamming game and the upper bound in terms of communication and perception performance is provided based on the Nash equilibrium to show the impact of the number of modalities and channel states. Simulation results based on the WaterScenes dataset and LLMs such as LLaVA show the performance gain of our proposed scheme with 13.6% higher perception accuracy

66.2% lower communication energy consumption and 21.7% less latency over benchmarks against the Q-learning-based smart jammer.

关键词

Keywords

references

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