Computationally Stable Low Sampling Rate Digital Predistortion for Non-Terrestrial Networks

With the advent of the fifth generation (5G) New Radio (NR), the Non-Terrestrial Network (NTN) stands out as a solution to enable wider coverage of broadcast satellites. NTN systems require higher data rates and bandwidth. Digital predistortion (DPD) is commonly adopted as an effective method to enhance the power efficiency of broadcast satellites' NTN systems. With the continuous increase of signal bandwidth, the bandwidth of the feedback loop and the sampling rate of analog-to-digital converters (ADCs) need to be reduced so as to reduce the system cost. The computational complexity and overfitting effect of the existing band-limited DPD (BLDPD) method will raise as the decrease of feedback bandwidth. To address this issue, one deep neural network (DNN) assisted band-limited polynomial digital predistortion (DNN-BLP DPD) is proposed in this paper. This method reduces the computational complexity and the overfitting effect of the band-limited basis functions by grouping a small number of band-limited basis functions for online parameter identification while embedding the DNN in the parameter identification module. Compared with the conventional BLDPD, the experimental results show that the proposed method can achieve a low sampling rate and low computational complexity while ensuring modeling accuracy.