A low-spur harmonic-cancelation digital predistortion based on neural network for frequency-hopping HF transmitters

High frequency (HF) transmitter implemented with the multioctave broadband power amplifier (PA) is always faced with the problem of interfering harmonics because of the nonlinearity of the PA. With the harmonic-cancelation digital predistortion (HC-DPD) scheme, the harmonics at lower frequencies can be canceled by the injected components and the harmonics at higher frequencies can be filtered out using a low-pass filter (LPF). However, the HC-DPD scheme brings the problem of unwanted spurs while lowering the requirements for sampling rate. In this article, a time-delay neural network (TDNN) model is used to replace the conventional memory polynomial (MP) model in the forward modeling. Theoretical derivation and simulation results validate the effectiveness of the TDNN model in harmonic cancelation and spur suppression. Further, a frequency-agile neural network (FANN) model is proposed based on the TDNN model. By adding the carrier frequency to the inputs of the network, the trained model can apply to all the trained carrier frequencies and is more friendly to the frequency-hopping scenarios. Experiments were carried out on a 2-30 MHz HF transmitter testbench. Measurement results show that the harmonic-to-fundamental power ratio, the adjacent channel power ratio (ACPR), and the error vector magnitude (EVM) performances were all improved by more than 20 dB. Compared with the MP model, the unwanted spurs can be suppressed by up to 22 dB. In addition, the number of total model coefficients of the proposed FANN model is only 22% of that of the TDNN model under the frequency-hopping scenario.