深度学习等分器与维特比-维特比算法连接,用于PAM D频段无线电通过光纤链接
Tangyao Xie1, Qiang Sheng2, Jianguo Yu1
1Beijing Key Laboratory of Work Safety Intelligent Monitoring, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Sensors (Basel, Switzerland)
|December 23, 2023
概括
这项研究提出了一种新的深度学习等级器,以减轻6G无线网络的D频段射频光纤 (ROF) 系统中的非线性问题. 拟议的等效器显著提高了性能,实现了低于HD-FEC值的错误率.
科学领域:
- 无线通信系统无线通信系统
- 光纤通信是指光纤通信.
- 机器学习应用程序 机器学习应用程序
背景情况:
- D频段 (110-170 GHz) 为6G网络提供了很大的带宽.
- 非线性效应和缺乏高频放大器是D频段ROF系统的关键挑战.
- 机器学习对于模拟通信系统中的非线性行为是有效的.
研究的目的:
- 为D频段ROF链接提出一种新的深度学习等分器.
- 通过将神经网络等分器与频率偏移估计 (FOE) 和载体相恢复 (CPR) 算法集成来降低计算负载.
- 评估实值神经网络 (RVNN) 和复杂值神经网络 (CVNN) 均等器的性能.
主要方法:
- 模拟的D频段45个Gbaud PAM-4和20个Gbaud PAM-8的ROF传输.
- 实现了与FOE和CPR算法相结合的深度学习均衡器.
- 采用连贯检测来提高接收器的灵敏度.
主要成果:
- 使用维特比-维特比算法的RVNN等分器显示出优异的非线性损伤补偿,特别是对于符号间干扰.
- 与维特比-维特比算法连接的CVNN和RVNN均可实现比特错误率 (BER) 低于HD-FEC值 (3.8 × 10−3).
- 拟议的方法有效地减轻了高速D频段ROF系统中的非线性效应.
结论:
- 深度学习神经网络等效器有效地减轻D频段ROF系统中的非线性损伤.
- 将RVNN/CVNN与维特比-维特比算法的集成为未来的6G无线网络提供了一个有希望的解决方案.
- 拟议的方法提高了BER性能,这对于可靠的高速数据传输至关重要.
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