Joint Symbol Rate-Modulation Format Identification and OSNR Estimation Using Random Forest Based Ensemble Learning for Intermediate Nodes
Joint Symbol Rate-Modulation Format Identification and OSNR Estimation Using Random Forest Based Ensemble Learning for Intermediate Nodes
复制标题
使用基于随机森林的中间节点集成学习的联合符号率调制格式识别和 OSNR 估计
DOI：
10.1109/jphot.2021.3117984
复制
发表时间：
2021-12-01
影响因子：
2.4
通讯作者：
Shi, Sheping
中科院分区：
文献类型：
--
作者：
Chai, Jia;Chen, Xue;Shi, Sheping
针对柔性密集波分复用(F-DWDM)网络中的中间节点，提出了一种基于低带宽相干检测和随机森林(RF)集成学习的符号速率调制格式识别(SR-MFI)和光信噪比(OSNR)联合估计方案。通过利用小体积波长扫描、无色散补偿和低复杂度射频的低带宽相干检测，该方案可以作为一种在F-DWDM网络的中间节点实现SR-MFI和OSNR联合估计的低复杂度和低成本的选择。为了验证所提方案的可行性，对8/16 Gbaud偏振分复用-4/16/32/正交幅度调制系统进行了综合仿真。仿真结果表明，SR-MFI的识别准确率达到100%，光信噪比估计的平均绝对误差在1dB以内。此外，通过8/16 Gbaud PDM-4/16/32QAM相干传输实验验证了所提出的监测方案。
In this paper, a novel joint symbol rate-modulation format identification (SR-MFI) and optical signal-to-noise ratio (OSNR) estimation scheme using the low-bandwidth coherent detecting and random forest (RF)-based ensemble learning is proposed for intermediate nodes in the flexible dense wavelength division multiplexing (F-DWDM) networks. By leveraging low-bandwidth coherent detecting with small bulk wavelength scanning, no chromatic dispersion compensation and low-complexity RF, the proposed scheme could serve as a reduced-complexity and cost-effective option to realize joint SR-MFI and OSNR estimation at intermediate nodes in F-DWDM networks. To verify the feasibility of the proposed scheme, the comprehensive simulations of 8/16 GBaud polarization division multiplexing (PDM)-4/16/32/64 quadrature amplitude modulation (QAM) systems are conducted. The simulation results show that the identification accuracy of SR-MFI reaches 100% and the mean absolute error of OSNR estimation is within 1 dB. Moreover, the proposed monitoring scheme is verified by 8/16 GBaud PDM-4/16/32QAM coherent transmission experiments.