论文标题
数字预性的残留神经网络
Residual Neural Networks for Digital Predistortion
论文作者
论文摘要
跟踪RF功率放大器（PA）的非线性行为具有挑战性。为了解决这个问题，我们在剩余学习与PA非线性之间建立了联系，并提出了一种新型的残留神经网络结构，称为残留的真实价值时间延迟神经网络（R2TDNN）。 R2TDNN没有学习PA的全部行为，而是通过在输入和输出层之间添加身份快捷方式连接来学习其非线性行为。特别是，我们将R2TDNN应用于数字预性，并在实际PA上测量实验结果。与Liu等人最近提出的神经网络相比。和Wang等人，R2TDNN在标准化的均方根误差和相邻的通道功率比方面达到了最佳的线性性能，其计算复杂性较小或相似。此外，R2TDNN表现出明显更快的训练速度和较低的训练错误。
Tracking the nonlinear behavior of an RF power amplifier (PA) is challenging. To tackle this problem, we build a connection between residual learning and the PA nonlinearity, and propose a novel residual neural network structure, referred to as the residual real-valued time-delay neural network (R2TDNN). Instead of learning the whole behavior of the PA, the R2TDNN focuses on learning its nonlinear behavior by adding identity shortcut connections between the input and output layer. In particular, we apply the R2TDNN to digital predistortion and measure experimental results on a real PA. Compared with neural networks recently proposed by Liu et al. and Wang et al., the R2TDNN achieves the best linearization performance in terms of normalized mean square error and adjacent channel power ratio with less or similar computational complexity. Furthermore, the R2TDNN exhibits significantly faster training speed and lower training error.
