A two-step approach for DLA-based digital predistortion using an integrated neural network

Abstract

In this study, we propose a two-step approach for direct-learning-architecture (DLA) based digital predistortion (DPD) using an integrated neural network. Because solutions to the DLA-based DPD cannot be obtained in closed form, an iterative search, which causes performance degradation and DPD divergence, is required. The proposed method employs an integrated neural network combining two sub-networks, namely, a DPD network and a power amplifier (PA) network, to find unknown solution. A one-dimensional convolutional neural network is adopted as the base structure for the DPD network to consider memory effects. The experimental results demonstrate that the proposed method reduces the adjacent channel leakage ratio by 4.3 dB and the error vector magnitude by 0.07, compared to the conventional method, and is stable over a long period without DPD coefficient update.