Low complexity and finer granularity quantization scheme for perturbation-based fiber nonlinearity compensation in coherent optical communication systems

In the efficient implementation of perturbation theory-based nonlinearity compensation method for reliable fiber-optic coherent communication systems, different quantization schemes have been proposed to reduce the required computational and implementation complexity. For example, the k-means clustering algorithm focused on reducing the number of perturbation coefficients to alleviate the burden of calculating the perturbation terms. In this paper, from the bit-level architectures of multipliers, we propose a multi-segment mixed-word-length quantization scheme that assigns different word lengths for the quantized perturbative coefficients to save computational resources, where Bayesian optimization is employed to derive the optimal hyperparameters involved in this scheme. The proposed scheme was evaluated by numerical simulations of single-channel and multi-channel optical fiber communication systems with dual-polarization 16-QAM modulation. The comprehensive numerical simulation results show that our proposed scheme demonstrates a complexity reduction of more than 65% without performance degradation compared with the traditional uniform quantization method with fixed-word-length, and was robust to the fiber length and baud-rate. We also carried out an experiment of single-carrier transmission over 18 x 100 km standard single-mode fiber with 20 Gbaud single-polarization 16-QAM signal. Similar to the numerical results, the hardware complexity can be reduced by 70.7% with negligible performance deterioration over the uniform quantization scheme. The proposed scheme shows great potential in real-world applications.