Li Yuzhe, Chang Huan, Zhang Qi, Gao Ran, Tian Feng, Tian Qinghua, Wang Yongjun, Rao Lan, Guo Dong, Wang Fu, Zhou Sitong, Xin Xiangjun
Appl Opt. 2024 Mar 1;63(7):1881-1887. doi: 10.1364/AO.517521.
The probabilistic shaping (PS) technique is a key technology for fiber optic communication systems to further approach the Shannon limit. To solve the problem that nonlinear equalizers are ineffective for probabilistic shaping optical communication systems with non-uniform distribution, a distribution alignment convolutional neural network (DACNN)-aided nonlinear equalizer is proposed. The approach calibrates the equalizer using the probabilistic shaping prior distribution, which reduces the training complexity and improves the performance of the equalizer simultaneously. Experimental results show nonlinear equalization of 120 Gb/s PS 64QAM signals in a 375 km transmission scenario. The proposed DACNN equalizer improves the receiver sensitivity by 2.6 dB and 1.1 dB over the Volterra equalizer and convolutional neural network (CNN) equalizer, respectively. Meanwhile, DACNN converges with fewer training epochs than CNN, which provides great potential for mitigating the nonlinear distortion of PS signals in fiber optic communication systems.
概率整形（PS）技术是光纤通信系统进一步逼近香农极限的关键技术。为了解决非线性均衡器对具有非均匀分布的概率整形光通信系统无效的问题，提出了一种分布对齐卷积神经网络（DACNN）辅助的非线性均衡器。该方法利用概率整形先验分布对均衡器进行校准，降低了训练复杂度，同时提高了均衡器的性能。实验结果表明，在375 km传输场景下实现了120 Gb/s PS 64QAM信号的非线性均衡。所提出的DACNN均衡器分别比沃尔泰拉均衡器和卷积神经网络（CNN）均衡器将接收机灵敏度提高了2.6 dB和1.1 dB。同时，DACNN比CNN收敛所需的训练轮次更少，这为减轻光纤通信系统中PS信号的非线性失真提供了巨大潜力。
