BPNN-based design of digital analysis filters for the aliasing errors cancellation in hybrid filter bank digital-to-analog converter

Abstract

This paper proposed back propagation neural network (BPNN)-based design of digital analysis filters to cancel the aliasing errors caused by the non-ideal characteristics of analog mixers and filters in hybrid filter bank digital-to-analog converter (HFB DAC). Initially, we establish a mathematical model for HFB DAC to derive the ideal and actual transfer functions, which is used to calculate the estimation error between the ideal and actual transfer functions. Then, the approximation error is obtained by summing the real and imaginary parts of the estimation error. Finally, the BPNN is used to minimize the approximation error, thereby obtaining the optimal coefficients for digital analysis filters to achieve the aliasing errors cancellation. In addition, this paper derives the computational complexity of BPNN. The simulation results show that our proposed BPNN-based design of digital analysis filters achieves better aliasing errors cancellation than the weighted least squares (WLS)-based, WLS+Optimization-based and minimax using second order cone programming (SOCP) designs at the cost of increased computational complexity.