通用知识与专用信息共驱动的具身智能抗干扰通信策略生成方法

齐佩汉; 刘智豪; 孙雯; 梁琳琳

doi:10.12263/DZXB.20260263

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通用知识与专用信息共驱动的具身智能抗干扰通信策略生成方法

学术论文 | 更新时间：2026-10-16

- 通用知识与专用信息共驱动的具身智能抗干扰通信策略生成方法
- Embodied Intelligent Anti-Jamming Communication Strategy Generation Method Co-Driven by General Knowledge and Specialized Information
- 电子学报 2026年54卷第6期页码：2727-2739
- 作者机构：
  
  1.西安电子科技大学，陕西西安 710000
  2.中兴通讯股份有限公司，陕西西安 710000
- 作者简介：
  
  齐佩汉男，1986年9月出生于河南省永城市。现任西安电子科技大学通信工程学院教授。主要研究方向为压缩感知、认知无线电网络频谱感知及智能信号处理。中国电子学会会员编号：E190027763M。E-mail: phqi@xidian.edu.cn
  刘智豪男，2002年1月出生于河南省三门峡市。现为西安电子科技大学博士研究生。主要研究方向为无线通信、具身智能和强化学习。E-mail: lzh7532022@163.com
  孙雯女，1987年12月出生于江西省南昌市。现为中兴通讯软件测试无线资深专家。主要研究方向为无线通信领域软件测试技术与质量保障。E-mail: sun.wen3@zte.com.cn
  梁琳琳男，1986年10月出生于河南省驻马店市。现任西安电子科技大学网络与信息安全学院副教授。主要研究方向为隐蔽通信、物联网应用与安全。E-mail: llliang@xidian.edu.cn
- 基金信息：
  
  国家自然科学基金(62171334);国防基础科研项目(JCKY2023110C099)
- DOI：10.12263/DZXB.20260263
  中图分类号： TN929.5;
- 收稿：2026-04-23，
  
  录用：2026-06-08，
  
  纸质出版：2026-06-25
- 稿件说明：
移动端阅览
齐佩汉, 刘智豪, 孙雯, 等. 通用知识与专用信息共驱动的具身智能抗干扰通信策略生成方法[J]. 电子学报, 2026, 54(06): 2727-2739.

QI Peihan, LIU Zhihao, SUN Wen, et al. Embodied Intelligent Anti-Jamming Communication Strategy Generation Method Co-Driven by General Knowledge and Specialized Information[J]. Acta Electronica Sinica, 2026, 54(06): 2727-2739.
齐佩汉, 刘智豪, 孙雯, 等. 通用知识与专用信息共驱动的具身智能抗干扰通信策略生成方法[J]. 电子学报, 2026, 54(06): 2727-2739. DOI：10.12263/DZXB.20260263

QI Peihan, LIU Zhihao, SUN Wen, et al. Embodied Intelligent Anti-Jamming Communication Strategy Generation Method Co-Driven by General Knowledge and Specialized Information[J]. Acta Electronica Sinica, 2026, 54(06): 2727-2739. DOI：10.12263/DZXB.20260263

摘要

随着新一代无线网络向通信、感知、计算一体化的复杂网络演进，通信设备呈现爆发式增长。海量设备的并发接入引发了激烈的频谱冲突，叠加开放信道下复杂多变的电磁干扰威胁，导致大规模网络协同通信困难。现有基于多智能体的抗干扰算法虽能缓解上述冲突，但其优化参数往往随智能体数量呈线性或指数级增加，庞大的训练开销与内存消耗使其难以实现大规模扩展。针对面临的协同困难与扩展性受限问题，提出了一种通用知识与专用信息共驱动的具身智能抗干扰通信策略生成方法。该方法通过参数共享策略构建统一的共享神经网络架构，将优化参数的增长与智能体数量相分离，在显著降低模型参数规模的同时提升系统的可扩展性。在此基础上，利用共享卷积神经网络（Convolutional Neural Network，CNN）对电磁环境进行特征提取，从原始频谱观测中学习可供所有智能体共同使用的通用知识表示。同时，为区分个体，方法进一步引入可学习的身份嵌入向量作为专用信息，并与通用知识进行联合建模，从而实现同质智能体的差异化决策，进而生成高效的协同抗干扰通信策略。仿真结果表明，该方法在固定干扰、扫频干扰和智能干扰场景下均具有显著优势，有效缓解了大规模抗干扰通信中的协同冲突，特别是在扫频干扰场景下，该方法的自适应通信效率（Self-Adaptive Communication Efficiency，SACE）较多智能体近端策略优化（Multi-Agent Proximal Policy Optimization，MAPPO）方法和独立DQN（Independent Deep Q-Network，IDQN）方法分别提升约22%和38%，验证了协同机制的高效性。此外，在不改变网络架构的前提下，针对不同规模的多信道多用户场景进行仿真实验验证，结果表明该方法能够稳定收敛并达到优异性能，具有良好的可扩展性。

Abstract

As new-generation wireless networks evolve toward complex networks integrating communication

sensing

and computing

the number of communication devices is experiencing explosive growth. The concurrent access of massive devices triggers intense spectrum conflicts

which

compounded by complex and dynamic electromagnetic interference threats in open channels

leads to difficulties in collaborative communication within large-scale networks. Although existing multi-agent-based anti-jamming algorithms can alleviate these conflicts

their optimization parameters often increase linearly or exponentially with the number of agents

and the resulting massive training overhead and memory consumption make large-scale expansion difficult. To address the challenges of collaboration and limited scalability

this paper proposes an embodied intelligent anti-jamming communication strategy generation method co-driven by general knowledge and specific information. This method constructs a unified shared neural network architecture through a parameter-sharing strategy

decoupling the growth of optimization parameters from the number of agents

thereby significantly reducing the scale of model parameters while enhancing system scalability. On this basis

a shared convolutional neural network (CNN) is utilized to extract features from the electromagnetic environment

learning general knowledge representations from raw spectrum observations that are available to all agents. Meanwhile

to distinguish individuals

the method introduces learnable identity embedding vectors as specific information and performs joint modeling with general knowledge

thereby achieving differentiated decision-making for homogeneous agents and generating efficient collaborative anti-jamming communication strategies. Simulation results demonstrate that this method offers significant advantages in fixed jamming

sweep jamming

and intelligent jamming scenarios

effectively alleviating collaborative conflicts in large-scale anti-jamming communication; particularly in the sweep jamming scenario

the self-adaptive communication efficiency (SACE) of this method improves by approximately 22% and 38% compared to the multi-agent proximal policy optimization (MAPPO) method and the independent deep Q-network (IDQN) method

respectively

verifying the efficiency of the collaborative mechanism. Furthermore

simulation experiments on multi-channel multi-user scenarios of varying scales

conducted without altering the network architecture

show that the method achieves stable convergence and excellent performance

demonstrating good scalability.

关键词

Keywords

references

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