Optical Phase Conjugation With Complex-Valued Deep Neural Network for WDM 64-QAM Coherent Optical Systems

We experimentally demonstrated a photoelectric non-linear compensation scheme of optical phase conjugation (OPC) with complex-valued deep neural network (CVDNN) to mitigate fiber nonlinearity in wavelength division multiplexing (WDM) 64-QAM coherent optical transmission system. The factors to affect the performance of OPC and CVDNN are comprehensively considered. OPC in WDM system is experimentally optimized to alleviate the deployment requirements of strict symmetrical distributed power and chromatic dispersion. The performance penalty caused by the simplification of the OPC is further compensated by the CVDNN. The selections of the input neurons' number and the optimization algorithm are also considered to design a simple two-hidden-layer-structure CVDNN. The proposed method is experimentally verified and evaluated in a 12.5-GBd 4-channel WDM 64-QAM 160-km standard single-mode fiber (SSMF) transmission system with channel spacing of 50-GHz. The experimental results show that the proposed nonlinear equalizer based on the OPC with CDVNN has a strong robustness to the input signal power and wavelength, which can not only improve the Q factor of the signal by 1.5-dB, but also greatly increase the total launched signal power.

Automated summary

In this study, an experimental demonstration of a photoelectric non-linear compensation scheme of optical phase conjugation (OPC) with complex-valued deep neural network (CVDNN) was successfully implemented in a 64-QAM coherent optical transmission system. The proposed method effectively mitigates the impact of fiber nonlinearity on the system, improving the signal Q factor by 1.5 dB and significantly increasing the total launched signal power.