宽带PA非线性的神经网络辅助DPD,用于亚尼奎斯特采样系统
Mengqiu Liu1, Xining Yang2, Jian Gao2
1Hangzhou Institute of Technology, Xidian University, Hangzhou 311200, China.
Sensors (Basel, Switzerland)
|February 26, 2025
概括
本研究介绍了一种新型的神经网络辅助的数字预扭曲方法,用于子尼奎斯特采样系统中的功率放大器. 该技术有效地解决了不足样本的信号,改善了功率频谱和错误向量大小等性能指标.
科学领域:
- 电气工程 电气工程
- 信号处理 信号处理
- 通信工程 通信工程
背景情况:
- 传统的数字预变形 (DPD) 需要高的采样率,这对亚尼奎斯特采样系统构成挑战.
- 这些系统中的低采样信号引入了模糊性,使DPD设计复杂化.
研究的目的:
- 提出一个神经网络 (NN) 辅助宽带功率放大器 (PA) DPD方法用于亚尼奎斯特采样系统.
- 为了应对设计用于样本不足信号的DPD的挑战,并提高PA性能.
主要方法:
- 采用双阶段架构来处理亚样本信号.
- 第一个阶段使用时间延迟的多项式重建来粗略估计非线性.
- 第二阶段使用NN进行虚拟DPD培训,利用预估模型和输入数据.
主要成果:
- 拟议的方法有效地解决了亚尼奎斯特系统中的宽带PA非线性.
- 它大大减少了所需的训练序列长度.
- 在功率频谱,错误向量大小和比特错误率方面优于传统方法.
结论:
- 在NN辅助的DPD方法是高效和有效的,对于亚Nyquist采样系统.
- 这种方法提供了一种可行的解决方案,用于在低样本条件下改善PA性能.
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