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基于学习的IRS辅助安全传输为我的IoT.

Minghui Min1,2, Jiayang Xiao1, Peng Zhang1

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概括

本研究介绍了一种智能反射表面 (IRS) 辅助系统,以改善矿山物联网 (MIoT) 设备的安全通信. 拟议的强化学习方法在具有挑战性的采矿环境中提高了传输可靠性和安全性.

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物联网的物联网,就是物联网.主动窃听 偷偷偷听 偷偷听智能反射表面是一个智能反射表面.采矿 采矿 采矿 采矿 采矿 采矿强化学习是一种强化学习.

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科学领域:

  • 电气工程 电气工程
  • 计算机科学 计算机科学
  • 采矿工程 采矿工程 采矿工程

背景情况:

  • 采矿物联网 (MIoT) 设备在具有挑战性的采矿环境中面临信号减弱和安全威胁.
  • 矿山中的无线通信容易受到窃听和干扰,影响安全和数据完整性.
  • 现有的解决方案难以应对地下采矿的动态和恶劣条件.

研究的目的:

  • 为了提高智能矿山中MIoT设备的无线通信的安全性和可靠性.
  • 开发一个智能反射表面 (IRS) 辅助的安全传输系统,适合采矿环境.
  • 为了解决MIoT通信中的信号减弱和对抗性威胁.

主要方法:

  • 为IRS相位转移和传输功率制定了一个联合优化问题,以改善合法传输和抑制窃听.
  • 基于强化学习 (RL) 的方案使用深度决定性政策梯度 (DDPG) 算法被建议用于动态通道条件.
  • DDPG算法优化了IRS反射系数和传输功率,以在连续空间中实现最佳传输政策,减少分离化错误.

主要成果:

  • 与没有IRS (IF) 和随机配置的IRS (IRC) 方案相比,拟议的IRS辅助的安全传输方案展示了优越的系统实用性.
  • 基于RL的方法有效地适应时间变化的通道条件,实时优化传输政策.
  • 模拟结果验证了拟议系统的增强安全性和可靠性.

结论:

  • 在MIoT无线通信中实施IRS显著提高了采矿行业的安全,保安和效率.
  • 拟议的基于RL的IRS辅助系统为在具有挑战性的采矿环境中安全MIoT通信提供了实用和有效的解决方案.
  • 这项研究为提高智能采矿基础设施的弹性提供了一个强大的框架.