Low-Feedback Sampling Rate Digital Predistortion Using Deep Neural Network for Wideband Wireless Transmitters

Abstract

In this paper, a low-feedback-sampling-rate digital predistortion (DPD) method is proposed for wideband wireless transmitters and radio-frequency power amplifiers (PAs). This DPD method inserts a non-ideal real band-pass filter into the feedback loop to limit the feedback bandwidth. Meanwhile, to recover the band-limited feedback signal, it introduces a signal-recovery module that is based on the deep neural network (DNN). A data-preprocessing technique is proposed to reduce the amount of input data for the DNN and thereby significantly reducing its structural complexity. Also, the use of DNN makes it feasible to implement off-line training. Both the simplicity of the proposed DNN and the availability of off-line training reduce the system complexity in DPD. Experimental validation was performed on a PA driven by wideband orthogonal-frequency-division-multiplexing (OFDM) signals. The results demonstrated the superiority of the proposed method in under-sampling applications. The proposed DPD method achieved the same linearization performance as the state-of-art DPD methods while requiring a sampling rate that was approximately 5% less. Meanwhile, the proposed DPD method has been validated to have stronger anti-noise and generalization capabilities.