基于知识图和DQN的智能反干扰决策技术
Dadong Ni1, Xiaoqing Liu2, Junyi Du1
1National Key Laboratory of Complex Aviation System Simulation, Chengdu 610036, China.
Sensors (Basel, Switzerland)
|December 31, 2025
概括
这项研究引入了智能反干扰通信的新两阶段框架,提高了决策速度和适应性. 新方法显著提高了在动态环境中的反干扰成功率和传输效率.
科学领域:
- 人工智能的人工智能
- 通信工程 通信工程
- 电磁兼容性 电磁兼容性 电磁兼容
背景情况:
- 智能反干扰通信在强化学习的融合速度和知识图的动态交互方面面临限制.
- 复杂,时间变化的电磁环境对现有的反干扰方法构成挑战.
研究的目的:
- 提出一个新的两阶段智能决策框架,以克服当前防干扰通信方法的局限性.
- 为了增强实时响应,模型进化和在动态电磁环境中的自我适应.
主要方法:
- 为快速,高效的推理和实时决策建立了一个防干扰知识图库.
- 引入了一个分层的强化学习架构,用于持续的环境交互和模型适应.
- 简化多维参数空间变成二维场景,以减少计算复杂性和加速融合.
主要成果:
- 与最先进的方法相比,反干扰决策成功率提高了4.2%.
- 在传输速率上表现出104.8%的改善.
- 在动态电磁环境中验证了优异的抗干扰性能和学习效率.
结论:
- 拟议的两阶段框架有效地解决了现有的智能反干扰通信方法的局限性.
- 该框架在动态电磁环境中提供了实用的有效性和卓越的性能,提高了成功率和学习效率.
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