深度增强学习驱动干扰增强安全无人机通信
Zhifang Xing1, Yunhui Qin2, Changhao Du3
1School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China.
Sensors (Basel, Switzerland)
|November 27, 2024
概括
这项研究通过使用干扰技术来增强无人机 (UAV) 通信安全. 多代理强化学习显著提高了对窃听的最低保密率.
科学领域:
- 无线通信无线通信
- 网络安全 网络安全
- 人工智能的人工智能
背景情况:
- 由于开放的无线通道,无人驾驶飞行器 (UAV) 通信面临着严重的窃听风险.
- 提高无人机通信系统的安全性和保密性对于可靠的数据传输至关重要.
研究的目的:
- 使用干扰技术优化无人机通信系统的最低保密率.
- 调查单代理和多代理强化学习算法的有效性,以确保无人机通信的安全.
主要方法:
- 制定优化用户协会,无人机轨迹和输出功率作为一个连续的决策问题.
- 采用单个代理软演员-关键 (SAC) 和双延迟深确定性政策梯度 (TD3) 算法.
- 在特定场景中使用多代理软演员-关键 (MASAC) 算法.
主要成果:
- 马萨克算法显示,正常化的平均分泌率显著增加.
- 在正常化的平均保密率中,MASAC的表现超过了SAC的6.6%,而TD3的表现超过了14.2%.
- 提出的基于干扰的方法有效地破坏了窃听的尝试.
结论:
- 多代理增强学习,特别是MASAC,在提高无人机通信安全方面提供了卓越的性能.
- 使用先进的AI算法对系统参数的联合优化有效提高了保密率.
- 基于干扰的安全机制对于强大的无人机通信网络至关重要.
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