一个查看表辅助BiLSTM神经网络基于无线通信基础设施的数字预变压器.
Reem Al Najjar1, Oualid Hammi1
1Department of Electrical Engineering, College of Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
这项研究引入了一种新型混合预扭曲器 (LUT-A-BiNN),通过将查找表与双向长短期记忆 (BiLSTM) 神经网络相结合来改善数字预扭曲. 这种方法将5G信号的相邻通道泄漏率提高5dB.
科学领域:
- 电气工程 电气工程
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 神经网络在数字预扭曲方面提供了卓越的性能,因为它们能够建模非线性系统.
- 现有的预扭曲技术很难有效地弥补功率放大器 (PA) 中的静态和动态非线性.
研究的目的:
- 为了提出和验证一种新型的混合前驱器,查找表辅助双向长短期记忆 (LUT-A-BiNN) 神经网络.
- 为了利用一个级联架构,一个查找表处理静态扭曲,一个BiLSTM网络处理动态扭曲.
主要方法:
- 设计了一个混合前驱器架构,将一个查找表 (LUT) 与一个双向长短期记忆 (BiLSTM) 神经网络相结合.
- LUT-A-BiNN模型通过5G测试信号进行实验验证,以评估其预变形性能.
- 性能与单盒BiLSTM神经网络预扰波器进行了比较.
主要成果:
- 拟议的LUT-A-BiNN预波器在相邻通道泄漏率 (ACLR) 中比起独立的BiLSTM预波器实现了5dB的改进.
- 混合方法有效地分离和补偿静态和动态非线性扭曲.
- 保持了BiLSTM的信号不可知性能特征.
结论:
- LUT-A-BiNN模型在非线性系统的数字预扭曲方面取得了重大进展,特别是PA.
- 这种混合方法为补偿复杂扭曲提供了更有效的解决方案,从而提高了光谱效率.
- 经过验证的性能证明了这种新型前驱器在5G等现代通信系统中的实际适用性.
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