基于强化学习的秘密通信的合作干扰和继电器选择
Jin Qian1, Hui Li1, Pengcheng Zhu1
1College of Information Engineering, Taizhou University, Taizhou 225300, China.
Sensors (Basel, Switzerland)
|October 16, 2025
概括
本研究引入了强化学习框架,以加强隐蔽的无线通信. 它优化了继电器选择和干扰策略,优于安全数据传输的随机方法.
科学领域:
- 无线通信安全 无线通信安全
- 机器学习应用 机器学习应用
- 网络优化 网络优化
背景情况:
- 在无线网络中保持秘密传输是具有挑战性的,因为有协作监护人.
- 现有的方法与动态的网络条件和对抗性威胁作斗争.
研究的目的:
- 开发一个强化学习框架,以优化秘密无线网络中的合作干扰和中继选择.
- 为了提高隐蔽吞吐量,并确保监狱监狱检测在电力限制下失败的概率.
主要方法:
- 开发了一个基于强化学习的继电器选择方案 (RLRS).
- 该框架共同优化中继转发和合作干扰策略.
- 该方法在多继电器辅助的双跳网络中进行了评估.
主要成果:
- RLRS显著超过了随机中继选择 (RRS).
- 实现了更高的峰值隐蔽传输速率和更低的中断概率.
- 证明了对不同网络参数 (如继电器密度和噪声) 的卓越适应性.
结论:
- 强化学习对于优化安全的秘密通信是有效的.
- 拟议的框架增强了网络安全,防止监护人的勾结.
- 该研究验证了RL用于动态继电器和干扰器选择的使用.
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