基于神经网络的最佳和可适应的功率分配,用于使用Jetson nano nano的实时FSO-RF通信.
Mahdi Akbari1, Saeed Olyaee2,3, Gholamreza Baghersalimi4
1Faculty of Electrical Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran.
Scientific reports
|November 12, 2025
概括
本研究介绍了一种使用深度神经网络 (DNN) 为光电无线网络的快速实时功率分配系统. 这种新的系统实现了高精度和速度,超过了传统的分析方法.
科学领域:
- 无线通信网络是无线通信网络.
- 优化算法的优化算法
- 深度学习应用程序深度学习应用程序
背景情况:
- 功率分配 (PA) 对于管理光电无线网络至关重要,但分析方法很慢.
- 深度神经网络 (DNN) 为高精度的实时PA提供了一个潜在的解决方案.
研究的目的:
- 开发和实施一个新的,实时的最佳功率分配 (PA) 系统,用于自由空间光学-无线电频 (FSO-RF) 无线网络.
- 利用DNN实现快速准确的PA,克服传统分析方法的局限性.
主要方法:
- 开发了一个实时最佳的PA系统,使用两个平行三层DNN用于FSO-RFPA.
- 使用加权最小平均平方误差 (WMMSE) 算法,对射频频道训练数据进行淡化和用户优先级.
- 采用分析性比特错误率 (BER) 算法来调整FSO通道的发射器功率间隔,考虑各种调制方案和设备大小.
- 在Jetson Nano平台上实现了DNN,用于性能评估.
主要成果:
- 实施的基于DNN的系统实现了1.6Gbps的总速率和97.82%的RF频道平均准确度,跨不同用户负载.
- FSO通道组件达到1.6 Gbps的数据速率,平均准确率为98.87%.
- 该系统在分析方法上表现出了显著的速度和精度优势.
结论:
- 拟议的基于DNN的PA系统为FSO-RF无线网络中的实时最佳PA提供了可行和高效的解决方案.
- 该系统的高精度和速度使其适合在苛刻的无线通信场景中实际部署.
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