Optimal Neural Network Hyperparameter Consideration through Digital Predistortion in RF Power Amplifiers

Abstract

This study introduces an effective optimization technique for identifying the optimal hyperparameters of a deep neural network (DNN) designed to model the behavior of power amplifiers (PAs). Hence, an innovative and efficient yield-analysis-based approach is proposed to improve both the modeling accuracy and the digital predistortion (DPD) performance of PAs. The method leverages a long short-term memory (LSTM) DNN architecture to capture the nonlinear and memory effects inherent in PA systems, thereby enhancing overall performance and linearization capability. To achieve optimal training, multiple optimization strategies are systematically applied to determine the most suitable hyperparameters, such as learning rate, network depth, and neuron configuration. To validate the effectiveness of the proposed method, a PA operating in the 1.8 GHz to 2.2 GHz frequency range is considered, for which extensive simulations and evaluations demonstrate that the optimized DNN achieves minimal modeling error while significantly improving DPD performance. The results confirm that the proposed framework offers a reliable and efficient solution for PA modeling and linearization, outperforming conventional techniques in terms of accuracy and consistency.