Tang Du, Wu Zhen, Sun Zhongliang, Tang Xizi, Qiao Yaojun
Opt Express. 2021 Oct 25;29(22):36242-36256. doi: 10.1364/OE.439362.
A novel joint intra and inter-channel nonlinearity compensation method is proposed, which is based on interpretable neural network (NN). For the first time, conventional cascaded digital back-propagation (DBP) and nonlinear polarization crosstalk canceller (NPCC) are deep unfolded into an NN architecture together based on their physical meanings. Verified by extensive simulations of 7-channel 20-GBaud DP-16QAM 3200-km coherent optical transmission, deep-unfolded DBP-NPCC (DU-DBP-NPCC) achieves 1 dB and 0.36 dB Q factor improvement at the launch power of -1 dBm/channel compared with chromatic dispersion compensation (CDC) and cascaded DBP-NPCC, respectively. Under the bit error rate threshold of 2 × 10, DU-DBP-NPCC extends the maximum transmission reach by 28% (700 km) compared with CDC. Besides, 3 different training schemes of DU-DBP-NPCC are investigated, implying the effective signal-to-noise ratio is not the proper evaluation metric of nonlinearity compensation performance for DU-DBP-NPCC. Moreover, DU-DBP-NPCC costs 26% lower computational complexity compared with DBP-NPCC, providing a better choice for joint intra and inter-channel nonlinearity compensation in long-haul coherent systems.
提出了一种基于可解释神经网络（NN）的新型联合通道内和通道间非线性补偿方法。首次基于传统级联数字反向传播（DBP）和非线性偏振串扰消除器（NPCC）的物理意义，将它们一起深度展开为一个神经网络架构。通过对7通道20-Gbaud DP-16QAM 3200公里相干光传输的大量仿真验证，与色散补偿（CDC）和级联DBP-NPCC相比，深度展开的DBP-NPCC（DU-DBP-NPCC）在-1 dBm/通道的发射功率下，Q因子分别提高了1 dB和0.36 dB。在误码率阈值为2×10时，与CDC相比，DU-DBP-NPCC将最大传输距离延长了28%（700公里）。此外，研究了DU-DBP-NPCC的3种不同训练方案，这意味着有效信噪比不是DU-DBP-NPCC非线性补偿性能的合适评估指标。此外，与DBP-NPCC相比，DU-DBP-NPCC的计算复杂度降低了26%，为长距离相干系统中的联合通道内和通道间非线性补偿提供了更好的选择。
