系统工程与电子技术 ›› 2025, Vol. 47 ›› Issue (6): 2055-2064.doi: 10.12305/j.issn.1001-506X.2025.06.33

• 通信与网络 • 上一篇    下一篇

意图态势双驱动的数据链抗干扰通信机制

刘书含1, 李彤1, 李富强2, 杨春刚1,*   

  1. 1. 西安电子科技大学通信工程学院, 陕西 西安 710071
    2. 中国电子科技集团公司数据链技术重点实验室, 陕西 西安 710068
  • 收稿日期:2024-05-20 出版日期:2025-06-25 发布日期:2025-07-09
  • 通讯作者: 杨春刚
  • 作者简介:刘书含 (2000—), 女, 硕士研究生, 主要研究方向为意图驱动网络智能决策、数据链网络多维资源感知
    李彤 (1999—), 女, 博士研究生, 主要研究方向为意图驱动网络管理、无线自组织网络、通信协议设计
    李富强 (1976—), 男, 研究员, 硕士, 主要研究方向为数据链网络
    杨春刚 (1982—), 男, 教授, 博士, 主要研究方向为人工智能信息通信网络、意图驱动网络

Intent and situation-dual driven anti-jamming communication mechanism for data link

Shuhan LIU1, Tong LI1, Fuqiang LI2, Chungang YANG1,*   

  1. 1. School of Telecommunications Engineering, Xidian University, Xi'an 710071, China
    2. Key Laboratory of Data Link Technology, China Electronics Technology Group Corporation, Xi'an 710068, China
  • Received:2024-05-20 Online:2025-06-25 Published:2025-07-09
  • Contact: Chungang YANG

摘要:

针对高动态、强对抗场景下数据链整体抗干扰效应差和自主适变能力弱的问题, 提出意图态势双驱动的数据链抗干扰通信机制。从架构、协议和技术3个角度出发, 提出分域、分级、分布式的抗干扰网络架构, 构建意图态势双驱动的跨层自适应抗干扰协议模型, 设计意图态势双驱动的智能抗干扰决策算法。将动态干扰环境下的多域联合抗干扰问题建模为马尔可夫决策过程, 并采用深度强化学习算法求解时、空、频多维联合抗干扰传输策略。仿真结果表明, 所提机制的网络吞吐量和传输成功率相较于其他方法分别提高了26.7%和54.5%, 显著提升了抗干扰传输性能。

关键词: 数据链网络, 意图态势双驱动, 抗干扰通信, 深度强化学习

Abstract:

In response to the challenges of poor anti-jamming effect, weak self-adaptive ability of data link in high dynamic and strong confrontation scenarios, an intent and situation-dual driven anti-jamming communication mechanism for data link networks is proposed. Covering architecture, protocol, and technology, a domain-based hierarchical distributed anti-jamming network architecture is presented, which constructs a cross-layer adaptive anti-jamming protocol model driven by intent and situation and designs an intelligent anti-jamming decision algorithm driven by intent and situation. The multi-domain joint anti-jamming problem in a dynamic jamming environment is modeled as a Markov decision process, and the deep reinforcement learning algorithm is used to solve the time, space, frequency multi-dimensional joint anti-jamming transmission policy. The simulation results show that the network throughput and transmission success rate of the proposed mechanism are improved by 26.7% and 54.5% respectively compared with other methods, and the proposed mechanism significantly improve the anti-jamming transmission performance.

Key words: data link network, intent and situation-dual driven, anti-jamming communication, deep reinforcement learning

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