Estimating dynamic nonlinear models of time-interleaved ADC in reproducing kernel Hilbert space

This paper proposes a model for Time-Interleaved ADC (TIADC) using the Volterra series to characterize the dynamic nonlinearities in the TIADC system. The proposed model serves as a general representation of TIADC mismatches. However, the use of Volterra series is limited to weakly nonlinear systems because the number of parameters increases exponentially. To reduce the complexity of measuring parameters of the proposed model, we recast the problem in reproducing kernel Hilbert space (RKHS) by using polynomial kernels. The effectiveness of the proposed method is assessed through simulations.