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双通道DNN等分器使用平衡随机过量采样为W频段PS-16QAM RoF交付超过4.6公里
Sicong Xu1, Bohan Sang1, Lingchuan Zeng2
1Shanghai Institute for Advanced Communication and Data Science, Key Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai 200433, China.
深度学习等分算法解决了W频段无线系统中的非线性. 一种具有随机超采样 (ROS) 的新型双通道深度神经网络 (DNN) 均衡器提高了性能,并降低了毫米波 (mm-wave) 传输的复杂性.
科学领域:
- 无线通信无线通信
- 光纤通讯是指光纤通讯的一种方式.
- 机器学习 机器学习
背景情况:
- W频段 (75-110 GHz) 远程毫米波无线传输系统的非线性来自光电设备,光纤和功率放大器.
- 深度学习等分算法可以减轻这些非线性,而概率塑造 (PS) 可以增加道容量.
- 变化的概率分布的m-平方幅度调制 (m-QAM) 与幅度创建不平衡的数据,阻碍有效的非线性均等.
研究的目的:
- 为了克服不平衡的机器学习问题,在W频段无线系统的非线性等效.
- 提出和验证一种新的双通道深度神经网络 (TLD) 均衡器,与随机过量采样 (ROS) 结合.
主要方法:
- 开发一个双通道深度神经网络 (TLD) 均衡器.
- 在接收器集成随机过量采样 (ROS),以解决不平衡的数据.
- 实验验证使用4.6公里无线电光纤 (ROF) 传输系统与W频段毫米波PS-16QAM.
主要成果:
- 拟议的TLD-ROS方案改善了W波段毫米波PS-16QAM系统在100米光纤和4.6公里无线距离上的整体系统性能.
- 与没有ROS的典型TLD相比,接收器的灵敏度提高了1dB.
- 实现了45.6%的复杂性减少和15.5%的培训样本减少.
结论:
- 发射器上的PS和接收器上的ROS的组合有效地增强了W频段无线传输系统.
- 该TLD-ROS等级器在接收器灵敏度,复杂性降低和训练效率方面提供了显著的改进.
- 深度学习和均衡的数据预处理技术的联合应用对于实际的无线物理层要求是非常有益的.
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