通过LWDDPG算法在EH-CIoT网络中的反干扰资源分配方法
Fushuai Li1, Jiawang Bao1, Jun Wang1
1College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China.
Sensors (Basel, Switzerland)
|August 29, 2024
概括
本研究介绍了一种对能源采集认知物联网网络的反干扰方法,以最大限度地提高长期吞吐量. 拟议的线性加权深度决定性政策梯度算法有效地分配资源,并在干扰攻击下收集能量.
科学领域:
- 无线通信网络 无线通信网络
- 网络安全 网络安全
- 资源管理 资源管理
背景情况:
- 收集能源的认知物联网 (EH-CIoT) 网络由于干扰攻击而面临吞吐量退化.
- 现有的方法缺乏适应未知的干扰器策略和主要用户活动的适应性.
研究的目的:
- 为EH-CIoT网络开发一种防止干扰的资源分配方法.
- 为了最大限度地提高长期吞吐量 (LTT),尽管阻塞和能源限制.
主要方法:
- 在没有事先知识的情况下,将问题建模为马尔科夫决策过程 (MDP).
- 设计一个包含吞吐量和能量奖励的二维奖励函数.
- 建议使用线性加权深度决定性政策梯度 (LWDDPG) 算法来分配资源.
主要成果:
- LWDDPG算法有效地将通道,功率和工作模式分配给二级用户 (SU).
- 该方法使SUS能够在未干扰的频道上发射,并收集射频能量.
- 模拟结果验证了拟议的方法对传统方法的优越性,可以抵御多重干扰攻击.
结论:
- 开发的反干扰策略增强了EH-CIoT网络中的LTT.
- LWDDPG算法为动态和对抗性环境中的资源分配提供了有效的解决方案.
- 该方法平衡了吞吐量最大化与能源采集,以实现可持续的网络运行.
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