基于深度Q学习的缓冲器辅助继电器选择,以在双跳无线继电网络中实现可靠和安全的通信
Cheng Zhang1,2, Xuening Liao1,2,3, Zhenqiang Wu1,2
1Key Laboratory of Modern Teaching Technology, Ministry of Education, Xi'an 710062, China.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
这项研究引入了一种新的深度Q学习方案,用于缓冲辅助中继选择,提高了放大和向前网络中的通信可靠性和安全性. 拟议的方法有效地平衡了这些关键方面,优于现有的方案.
科学领域:
- 无线通信无线通信
- 网络安全 网络安全
- 机器学习应用 机器学习应用
背景情况:
- 无线信号容易色和窃听,损害通信完整性.
- 现有的继电器选择方案通常涉及可靠性或安全性,但不是两者兼而有之.
- 缓冲器辅助的放大和转发 (AF) 网络需要强大的战略来实现可靠和机密的数据传输.
研究的目的:
- 提出一种新的缓冲辅助继电器选择方案,将可靠性和安全性结合起来.
- 解决现有计划的局限性,这些计划专注于通信绩效的单一方面.
- 为了提高双跳 AF 无线网络的整体性能,防止色和窃听.
主要方法:
- 开发一种使用深度Q学习 (DQL) 的缓冲辅助继电器选择方案.
- 蒙特卡洛模拟用于评估拟议方案的性能.
- 与基准方案进行比较,包括最大比率方案.
主要成果:
- 拟议的基于DQL的方案有效地实现了在双跳AF网络中的可靠和安全通信.
- 与最大比率方案相比,该方案在保密中断概率 (SOP) 方面表现优越.
- 蒙特卡洛模拟验证了该方案在减轻连接中断概率 (COP) 和SOP方面的有效性.
结论:
- 拟议的基于深度Q学习的缓冲辅助继电器选择方案为可靠和安全的无线通信提供了全面的解决方案.
- 这种方法成功地平衡了AF网络中通信完整性和数据机密性的竞争要求.
- 这些发现突显了DQL在优化复杂无线网络功能方面的潜力.
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