Deep Neural Network-Based Digital Predistorter for Doherty Power Amplifiers

In this letter, measured adjacent channel leakage ratio (ACLR) results using a GaN Doherty power amplifier will show that for less than 2000 coefficients, sigmoid activated deep neural network (DNN)-based digital predistorter (DPD) outperforms rectified linear unit (ReLU) activation by up to 2 dB even when the number of layers of the network is increased. When the number of coefficients exceeds 2000 ReLU outperforms sigmoid activation with an improvement of up to 3-4 dB in ACLR suppression. Furthermore, to achieve an ACLR level of -54 dBc or better, the number of coefficients required to implement the DNN-DPD can be reduced by a factor of 150 when using ReLU rather than sigmoid activation.