Blind Adaptive Digital Backpropagation for Fiber Nonlinearity Compensation

Fully blind adaptive digital backpropagation (DBP) based on a steepest descent algorithm is proposed. For homogeneous links, a two-parameter blind adaptive (TBA) scheme is presented, which assumes that all DBP steps have the same nonlinear scaling factor and filter bandwidth parameter. For heterogeneous links, a multiparameter blind adaptive (MBA) scheme is proposed to achieve the optimal performance by adapting chromatic dispersion, nonlinear scaling factor, and bandwidth parameter of each DBP step. The principles and performance of the proposed schemes are presented for low-pass filter assisted DBP, denoted as LDBP, in single carrier systems and cross-phase modulation model based DBP in subcarrier-multiplexing (SCM) systems, denoted as SCM-DBP. In a 34.94 Gbaud dual-polarization 16QAM single channel transmission experiment with a homogeneous 2560 km link, we demonstrate that both TBA-LDBP and TBA-SCM-DBP can converge to optimal performance with various system configurations. In addition, for heterogeneous links, we show in simulations and experiments that MBA-LDBP and MBA-SCM-DBP achieve 0.2-1.4 dB improvement compared to their TBA counterparts.