认知学习支持敏捷光网络

IF 4 2区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Journal of Optical Communications and Networking Pub Date : 2024-10-30 DOI:10.1364/JOCN.538632
Yijun Cheng;Zejun Chen;Zihe Hu;Meng Xiang;Zhijun Yan;Yuwen Qin;Songnian Fu
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引用次数: 0

摘要

非线性均衡(NLE)对于保证光网络(ON)的性能至关重要。非线性均衡的有效实施依赖于传输链路的关键参数,包括调制格式(MF)和发射功率。随着光网络变得越来越灵活,光纤传输的参数需要自适应并与路由条件相关。因此,成功实施 NLE 有赖于实现传输感知 (TA)。尽管在过去几年中对机器学习支持的光性能监控(OPM)进行了广泛研究,但目前的 NLE 算法无法自主感知传输参数。此外,目前的 TA 实现仍然需要人工干预来指导 NLE。此外,现有的基于 ML 的 OPM 和 NLE 无法进行自主训练,导致无法应对环境变化和错误标记。在此,我们提出了认知学习(CL),用于在敏捷网络中由 TA 引导的 NLE。我们进行了一项实验,在 1500 千米标准单模光纤(SSMF)上进行 32 Gbaud 偏振分复用(PDM)-正交相移键控(QPSK)/16 正交振幅调制(QAM)传输,发射功率从 0 到 3 dBm 不等。在开发了以振幅直方图(AHs)为输入的深度神经网络(DNN)和每跨一步学习数字反向传播(1stps-LDBP)后,CL 可同时实现 TA 和 NLE,并具有自学习、抗误标记和动态适应能力。概念验证实验结果表明,当发射功率为 3 dBm 时,TA 的精度和 PDM-16QAM 的 Q 因子分别提高了 34.8% 和 0.84 dB。此外,即使所使用的数据存在 30% 的错误标记,TA 的准确度也能提高 35.3%。因此,CL 框架可以定制,以满足各种 NLE 实现,从而支持敏捷 ON 的自适应传输。
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Cognitive learning enabled agile optical network
Nonlinear equalization (NLE) is essential for guaranteeing the performance of an optical network (ON). Effective NLE implementation relies on key parameters of the transmission link, including the modulation format (MF) and the launch power. As ONs become more agile, the parameters of fiber optical transmission need to be adaptive and relevant to the routing condition. Therefore, successful NLE implementation relies on the realization of transmission awareness (TA). Although machine learning-enabled optical performance monitoring (OPM) has been extensively investigated in the past few years, current NLE algorithms cannot autonomously perceive transmission parameters. Furthermore, current TA implementation still needs human intervention to guide the NLE. In addition, existing ML-based OPM and NLE cannot be trained autonomously, leading to the incapability of environmental change and mislabeling. Here, we propose cognitive learning (CL) for TA-guided NLE in agile ONs. We perform an experiment involving 32 Gbaud polarization-division-multiplexed (PDM)-quadrature phase shift keying (QPSK)/16-quadrature amplitude modulation (QAM) transmission over 1500 km of standard single-mode fiber (SSMF) with a variable launch power from 0 to 3 dBm. When a deep neural network (DNN) with amplitude histograms (AHs) as inputs and one step per span-learned digital back-propagation (1stps-LDBP) are developed, the CL simultaneously enables both TA and NLE, with the capability of self-learning, mislabeling resistance, and dynamic adaptation. The proof-of-concept experimental results indicate that both the accuracy of TA and the Q-factor of PDM-16QAM can be improved by 34.8% and 0.84 dB, respectively, when the launch power is 3 dBm. Moreover, the accuracy of TA is enhanced by 35.3%, even when the used data has 30% mislabeling. Therefore, the CL framework can be customized to satisfy various NLE implementations, thereby supporting the adaptive transmission of agile ONs.
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来源期刊
CiteScore
9.40
自引率
16.00%
发文量
104
审稿时长
4 months
期刊介绍: The scope of the Journal includes advances in the state-of-the-art of optical networking science, technology, and engineering. Both theoretical contributions (including new techniques, concepts, analyses, and economic studies) and practical contributions (including optical networking experiments, prototypes, and new applications) are encouraged. Subareas of interest include the architecture and design of optical networks, optical network survivability and security, software-defined optical networking, elastic optical networks, data and control plane advances, network management related innovation, and optical access networks. Enabling technologies and their applications are suitable topics only if the results are shown to directly impact optical networking beyond simple point-to-point networks.
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