Digital Predistortion of Quadrature Digital Power Amplifiers Using RVRTCNN: Real-Valued Residual Temporal Convolutional Neural Network

Abstract

Deep neural networks (DNNs) predistortion models of radio frequency (RF) power amplifiers (PAs), while offering excellent performance, typically suffer from high parameter counts and computational complexity. Convolutional NNs (CNNs) have been introduced to reduce model complexity due to their weight-sharing characteristic. However, the inherent calculation mode of traditional convolutional structures limits their ability to effectively capture temporal dependencies within the data, hindering their effectiveness in addressing memory effects in PAs. In this letter, we propose an enhanced digital predistortion (DPD) model based on a real-valued residual temporal convolutional neural network (RVRTCNN) for quadrature digital PAs (QDPAs). The proposed model incorporates dilated convolutions to extract features across multiple time steps and capture complex temporal dependencies, thereby enhancing its ability to address the dynamic nonlinearity of PAs. A 15-bit transformer-based QDPA chip, integrating Class-G and IQ-cell-sharing techniques, was fabricated by 28 nm CMOS process to validate our proposed method. Experimental results demonstrate that the proposed model achieves superior linearization performance with significantly fewer parameters and lower computational complexity compared to state-of-the-art (SOTA) models, improving both adjacent channel power ratio (ACPR) and error vector magnitude (EVM) by over 10 dB for the 802.11ax 40 MHz 64-QAM signal.