Research on Neural Network Digital Pre-distorter Based on Transformer

The design of 5G communication systems requires wider transmission bandwidth and higher modulation levels, which necessitates the linearization of power amplifiers (PAs) to mitigate their nonlinearity. Neural network (NN) models, such as back propagation (BP) and long short term memory (LSTM), have been applied in the field of digital predistortion (DPD) and have become a new solution for radio frequency power amplifier (RF PA) linearization. This paper proposes a Transformer-based NN digital predistorter that utilizes the multi-head self-attention mechanism of the model to simultaneously capture the global temporal relationship and the dependency between signal features, constructing better signal nonlinear characteristics and memory effects for the current signal in the lookahead term. The model improves the prediction performance and parallelism of the model, accelerating the training speed. The experimental results show that the model improves the adjacent channel power ratio (ACPR) index by about 12dB compared to the output signal without DPD processing, and reduces the parameter volume by 57% compared to the LSTM network with similar accuracy. Meanwhile, the model's normalized mean square error (NMSE) index reaches -39dB, which is about 5dB higher than that of the traditional BP NN model.