Optimal design of digital FIR filters based on text convolutional neural network for aliasing errors reduction in FI-DAC

This paper presents optimal design of digital finite impulse response (FIR) filters based on text convolutional neural network (TEXTCNN) to reduce the aliasing errors in frequency interleaving digital-toanalog converters (FI-DAC). For an example of FI-DAC with M subDACs, we first obtained the approximation error function. We add the real and imaginary parts of the approximation error to obtain the error function. Finally, the task-relevant spectral features are extracted by the convolution operation of the TEXT-CNN model, and the parameters are updated by iterative training to obtain optimal coefficients of digital FIR filters. Additionally, we derived the computational complexity of our proposed TEXT-CNN architecture. Several design examples were given to verify the performance of our proposed optimal design based on TEXTCNN. The simulation results showed that, by using our presented optimal design based on TEXT-CNN, the maximum distortion error was 0.0016 dB, and the maximum aliasing error is-73.24 dB which satisfied the desired spurious free dynamic range (SFDR) in a 12-bit FI-DAC system. Further, the computational complexity of our presented optimal design based on TEXT-CNN is compared with other three optimal designs, our proposed TEXT-CNN optimal design can obtain better aliasing errors reduction at the cost of