Hu Xin, Liu Zhijun, Yu Xiaofei, Zhao Yulong, Chen Wenhua, Hu Biao, Du Xuekun, Li Xiang, Helaoui Mohamed, Wang Weidong, Ghannouchi Fadhel M
IEEE Trans Neural Netw Learn Syst. 2022 Aug;33(8):3923-3937. doi: 10.1109/TNNLS.2021.3054867. Epub 2022 Aug 3.
Power amplifier (PA) models, such as the neural network (NN) models and the multilayer NN models, have problems with high complexity. In this article, we first propose a novel behavior model for wideband PAs, using a real-valued time-delay convolutional NN (RVTDCNN). The input data of the model is sorted and arranged as a graph composed of the in-phase and quadrature ( I/Q ) components and envelope-dependent terms of current and past signals. Then, we created a predesigned filter using the convolutional layer to extract the basis functions required for the PA forward or reverse modeling. Finally, the generated rich basis functions are input into a simple, fully connected layer to build the model. Due to the weight sharing characteristics of the convolutional model's structure, the strong memory effect does not lead to a significant increase in the complexity of the model. Meanwhile, the extraction effect of the predesigned 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. Meanwhile, compared with the abovementioned models, the coefficient number and computational complexity of the RVTDCNN model are significantly reduced. This advantage is noticeable when the memory effects of the PA are increased by using wider signal bandwidths.
功率放大器（PA）模型，如神经网络（NN）模型和多层NN模型，存在复杂度高的问题。在本文中，我们首先提出了一种用于宽带PA的新型行为模型，使用实值时延卷积神经网络（RVTDCNN）。该模型的输入数据被整理并排列成一个由同相和正交（I/Q）分量以及当前和过去信号的包络相关项组成的图形。然后，我们使用卷积层创建了一个预设计滤波器，以提取PA正向或反向建模所需的基函数。最后，将生成的丰富基函数输入到一个简单的全连接层中以构建模型。由于卷积模型结构的权重共享特性，强记忆效应不会导致模型复杂度显著增加。同时，预设计滤波器的提取效果也降低了模型的训练复杂度。实验结果表明，RVTDCNN模型的性能与NN模型和多层NN模型几乎相同。同时，与上述模型相比，RVTDCNN模型的系数数量和计算复杂度显著降低。当通过使用更宽的信号带宽来增加PA的记忆效应时，这一优势尤为明显。
