使用自能回收和深度神经网络的AF全双流中继网络的安全可靠性分析.
Tan N Nguyen1, Bui Vu Minh2, Dinh-Hieu Tran3
1Communication and Signal Processing Research Group, Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City 70000, Vietnam.
Sensors (Basel, Switzerland)
|September 9, 2023
概括
本研究分析了传输电力和信息的无线网络的安全性和可靠性. 研究人员开发了新的方法来预测网络性能,并使用深度学习来获得准确的结果.
科学领域:
- 无线通信网络是无线通信网络.
- 信息理论是信息理论.
- 网络安全 网络安全
背景情况:
- 同时无线信息和电力传输 (SWIPT) 对于可持续的无线网络至关重要.
- 放大和转发 (AF) 完全双重 (FD) 继电器在平衡安全性和可靠性方面面临挑战.
- 电力分割 (PS) 协议通常用于继电器中收集能量.
研究的目的:
- 调查SWIPT辅助的AF-FD中继网络中的安全可靠性权衡.
- 开发用于评估网络性能的分析框架.
- 探索深度学习用于性能预测的应用.
主要方法:
- 为中断概率 (OP) 和拦截概率 (IP) 的封闭式公式的推导.
- 对OP和IP进行异面分析,以获取对系统参数的见解.
- 蒙特卡洛模拟用于验证分析模型.
- 开发一个深度神经网络 (DNN) 用于OP和IP预测.
主要成果:
- 对OP和IP的封闭式表达式得到了推导,为分析提供了理论基础.
- 非对称分析揭示了影响安全性和可靠性的关键系统参数.
- 蒙特卡洛模拟证实了分析结果的准确性.
- 一个DNN模型证明了性能预测的高精度和低复杂性.
结论:
- 该研究提供了对SWIPT辅助AF-FD中继网络安全可靠性的全面分析.
- 分析和模拟结果为网络设计和优化提供了宝贵的见解.
- 拟议的DNN为预测网络性能提供了一种有效的方法.
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