Augmented Convolutional Neural Network for Behavioral Modeling and Digital Predistortion of Concurrent Multiband Power Amplifiers

Neural network (NN)-based models are perceived as being accurate models for power amplifier (PA) behavioral modeling and digital predistortion (DPD) applications. However, the complexity of NN's models in terms of the number of coefficients increases with the memory depth, nonlinearity order, signal bandwidth, and the number of carriers. In this article, a novel augmented convolutional neural network (ACNN)-based DPD is proposed to linearize the concurrent multiband PAs. In the ACNN model, the convolution layer with a fully connected layer serves to model the intermodulation distortion (IMD), cross-modulation distortion (CMD), and nonlinearities of the concurrent multiband PA. The pooling layer in the ACNN reduces the model's complexity. The measurement results show that the proposed DPD requires significantly fewer coefficients and floating-point operations (FLOPs) than the state-of-the-art DPDs. This model's complexity reduction arises with the number of carriers in the multiband aggregated signal. The measurement results also show that the proposed DPD has a better linearization performance in terms of metrics, such as normalized mean square error (NMSE), adjacent channel power ratio (ACPR), and error vector magnitude (EVM).