Integrating Volterra Series Model and Deep Neural Networks to Equalize Nonlinear Power Amplifiers

Abstract

The nonlinearity of power amplifiers (PAs) has been one of the severe constraints to the performance of modern wireless transceivers. This problem is even more challenging for the fifth generation (5G) cellular system since 5G signals have extremely high peak to average power ratio. This paper develops nonlinear equalizers that exploit both deep neural networks (DNNs) and Volterra series models to mitigate PA nonlinear distortions. The DNN equalizer architecture consists of multiple one-dimension convolutional layers. The input features are designed according to the Volterra series model of nonlinear PAs. This enables the DNN equalizer to mitigate nonlinear PA distortions more effectively while avoiding over-fitting under limited training data. Experiments are conducted with both simulated data based on a Doherty nonlinear PA model and real measurement data obtained from a highly nonlinear cable TV PA. The results demonstrate that the proposed DNN equalizer has superior performance over conventional nonlinear equalization approaches.