MZM nonlinear equalization by sinusoidal subcarrier modulation combined with LM-BP neural network

In order to mitigate the nonlinear effects of Mach-Zehnder modulator (MZM) on optical transmission signals in intensity modulation and direct detection (IM-DD) systems, a combined approach utilizing sinusoidal subcarrier modulation (SSM) and the Levenberg-Marquardt back propagation (LM-BP) neural network is proposed in this paper. The method employs a sine wave as the subcarrier to carry the 4 pulse amplitude modulation (PAM4) signals, aiming to equalize the distorted signals after MZM modulation. Subsequently, the LM-BP algorithm eliminates any remaining inter-symbol interference (ISI). This scheme uses sine wave modulation to solve the problem of additional ISI caused by triangular wave modulation. Furthermore, this combined approach simplifies the algorithm complexity compared to solely relying on a neural network equalizer. In this paper, the performance of SSM-LM-BP scheme is simulated and analyzed in IM-DD system. The results show that the joint scheme outperforms the triangular wave modulation scheme as well as the neural network algorithm after transmitting 50 Gbit/s PAM4 signals for 80 km without relays under the conditions of dispersion compensation, and the symbol error rate (SER) can be as low as 10⁻⁵.