Low-Complexity Adaptive Digital Predistortion with Meta-Learning based Neural Networks

International audience; In this paper, we study a meta-learning based neural network (NN) model to enhance the energy-efficiency related to power amplification in wireless communication systems. Specifically, we introduce a low-complexity adaptive solution to perform digital predistortion (DPD) for power amplifiers (PAs) using neural networks. Thus, we design a dedicated NN architecture to derive predistortion functions using few neurons. Moreover, we develop a meta-learning training approach that allows better generalization and faster adaptation, over time-varying PAs, compared to classical DPD architectures. Thereby, we propose a new approach to realize an adaptive digital predisorter based on meta-learning using few samples for online calibration. A dedicated architecture allows to achieve low-complexity while meta-learning permits adapting to most parameters change in the system. Through the simulation results, we have shown that the developed meta-learning based NN DPD can offer a meta-trained DPD function, i.e., trained offline, that can provide satisfying performance for different PA models. Contrary to classical DPD architectures, the performance of our meta-trained DPD can be improved through only few gradient steps and over few samples during online calibration, achieving excellent performance with moderate complexity.