  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.
