Reduced Complexity Backpropagation-Based Adaptive Compensation of Frequency Interleaved ADC for Digital Communication Receivers

This work presents a reduced complexity architecture for the backpropagation-based compensation of frequency interleaved analog to digital converters (FI-ADC). Earlier literature has described a promising adaptive background compensation technique based on the backpropagation algorithm from machine learning, which is suitable to mitigate errors of the analog signal path of FI-ADCs. This technique is applicable to high speed digital receivers such as those used in coherent optical communications. The key ingredients of the aforementioned technique are MIMO equalization and the backpropagation algorithm used to adapt the coefficients of the equalizer. The work presented here modifies the earlier architecture to reduce the number of required analog mixers by a factor two, and to simplify, also by a factor two, the compensation equalizer responsible for correcting the errors of the analog signal path. This results in a much simpler and significantly improved FI-ADC system. Simulations show that the analog impairments are accurately compensated and their impact on the performance of the receiver is essentially eliminated.