Comparative Analysis of Neural Network Architectures for Digital Predistortion in Power Amplifier Linearization
TL;DR
Simulation results show that while CNNs offer a favorable trade-off between linearization performance and model complexity, GRU and LSTM architectures achieve the best overall NMSE and ACPR improvements, albeit with a higher number of parameters.
Abstract
In this study, we examine and contrast the effectiveness of different artificial neural network (ANN) topologies for power amplifier (PA) digital pre-distortion (DPD). In particular, we investigate long short-term memory (LSTM) networks, gated recurrent units (GRU), recurrent neural networks (RNN), con-volutional neural networks (CNN), and fully connected neural networks (DNN). For training and assessment, a dataset comprising measured input and output signals from a commercial NXP Doherty PA working in the 3.6–3.8 GHz region with a 16-QAM OFDM signal is utilised. Normalised mean squared error (NMSE), adjacent channel power ratio (ACPR), and model complexity are used to evaluate the models. Simulation results show that while CNNs offer a favorable trade-off between linearization performance and model complexity, GRU and LSTM architectures achieve the best overall NMSE and ACPR improvements, albeit with a higher number of parameters. Power spectral density and AM/AM characteristic analyses further confirm the superior linearization performance achieved using recurrent gated structures.