论文标题
用于行为建模和宽带功率放大器预期的卷积神经网络
Convolutional Neural Network for Behavioral Modeling and Predistortion of Wideband Power Amplifiers
论文作者
论文摘要
在本文中，我们使用实价的时间延迟卷积神经网络（RVTDCNN）提出了一个新型宽带PA的行为模型。模型的输入数据被排序和布置为由相期和正交（I/Q）组成的图形组成的图，以及当前和过去信号的包络依赖性项。我们使用卷积层设计了预设的过滤器，以提取PA向前或反向建模所需的基础功能。使用简单的完全连接层对生成的丰富基础函数进行建模。由于卷积结构的重量共享特性，强大的记忆效应并不能导致模型的复杂性明显增加。同时，预设计过滤器的提取效果还降低了模型的训练复杂性。实验结果表明，RVTDCNN模型的性能几乎与NN模型和多层NN模型相同。
In this paper, we propose a novel behavior model for wideband PAs using a real-valued time-delay convolutional neural network (RVTDCNN). The input data of the model are sorted and arranged as the graph composed of the in-phase and quadrature (I/Q) components and envelope-dependent terms of current and past signals. We design a pre-designed filter using the convolutional layer to extract the basis functions required for the PA forward or reverse modeling. The generated rich basis functions are modeled using a simple fully connected layer. Because of the weight sharing characteristics of the convolutional structure, the strong memory effect does not lead to a obvious increase in the complexity of the model. Meanwhile, the extraction effect of the pre-designed filter also reduces the training complexity of the model. The experimental results show that the performance of the RVTDCNN model is almost the same as the NN models and the multilayer NN models.
