在 IRS 辅助交织 CIoV 网络中增强物理层安全性:一种异质的多代理 Mamba RainbowDQN 方法
Ruiquan Lin1, Shengjie Xie1, Wencheng Chen1
1College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China.
Sensors (Basel, Switzerland)
|October 29, 2025
概括
这项研究通过智能反射表面 (IRS) 和深度强化学习来增强汽车互联网 (IoV) 的安全性. 这种新的方法显著提高了保密率,并降低了车辆到基础设施 (V2I) 通信中的保密中断概率.
科学领域:
- 通信工程 通信工程
- 网络安全 网络安全
- 人工智能的人工智能
背景情况:
- 车辆互联网 (IoV) 依赖于车辆对一切 (V2X) 进行合作感知,车辆对基础设施 (V2I) 链接至关重要.
- 在 IoV 中,开放的无线通道容易受到窃听的影响,从而危及隐私和安全.
研究的目的:
- 在智能反射表面 (IRS) 辅助交织认知IoV (CIoV) 网络中增强车辆与基础设施 (V2I) 通信的物理层安全性.
- 为了解决频谱分配,发射功率和IRS相位移的非凸合联合优化问题.
主要方法:
- 为频谱分配,车辆用户 (VU) 传输功率和IRS相位转移制定一个非凸起的联合优化问题.
- 开发一个异质的多代理 (HMA) Mamba RainbowDQN算法,同质的VU和异质的二次基站 (SBS) 作为不同的代理.
- 智能反射表面 (IRS) 和深度强化学习 (DRL) 的集成,以提高安全性.
主要成果:
- 拟议的HMA Mamba RainbowDQN算法显著优于基准方案.
- 在V2I通信的保密率上取得了13.29%的改善.
- 在秘密中断概率 (SOP) 中减少了54.2%.
结论:
- 在CIoV网络中,IRS和DRL的集成是安全和高效的V2I通信的有效途径.
- 拟议的算法成功地提高了物理层对窃听的安全性.
- 这项研究证实了先进的人工智能技术在保护IoV环境中的潜力.
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