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。
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 commu-nication 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 simu-lation 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.
作者
刘书含
李彤
李富强
杨春刚
LIU Shuhan;LI Tong;LI Fuqiang;YANG Chungang(School of Telecommunications Engineering,Xidian University,Xi’an 710071,China;Key Laboratory of Data Link Technology,China Electronics Technology Group Corporation,Xi’an 710068,China)
出处
《系统工程与电子技术》
北大核心
2025年第6期2055-2064,共10页
Systems Engineering and Electronics
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
data link network
intent and situation-dual driven
anti-jamming communication
deep reinforcement learning
