Modulation Format Identification and Transmission Quality Monitoring for Link Establishment in Optical Network Using Machine Learning Techniques

Hong, Jie; Chen, Long; Zhu, Jiao; Zhou, Wenhai; Li, Bo; Fu, Yongfeng; Wang, Long

doi:10.1364/acpc.2020.m4a.191

Cited by 2 publications

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“…With the ability of learning and adapt to changes in the environment, the ML technology can well cope with the challenges brought by dynamic optical networks and can even deal with unforeseen scenarios when the ML algorithm is designed [40]. Therefore, the ML algorithm is a promising solution to dynamic optical networks, with applications in optical performance monitoring [41][42][43][44][45][46][47][48][49], modulation format identification [50][51][52] and nonlinearity mitigation [53,54]. In the control plane, ML techniques have been used to achieve the highly demand dynamic control, reconfiguration, and optical network virtualization [39].…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Applications for Short Reach Optical Communication

et al. 2022

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With the rapid development of optical communication systems, more advanced techniques conventionally used in long-haul transmissions have gradually entered systems covering shorter distances below 100 km, where higher-speed connections are required in various applications, such as the optical access networks, inter- and intra-data center interconnects, mobile fronthaul, and in-building and indoor communications. One of the techniques that has attracted intensive interests in short-reach optical communications is machine learning (ML). Due to its robust problem-solving, decision-making, and pattern recognition capabilities, ML techniques have become an essential solution for many challenging aspects. In particular, taking advantage of their high accuracy, adaptability, and implementation efficiency, ML has been widely studied in short-reach optical communications for optical performance monitoring (OPM), modulation format identification (MFI), signal processing and in-building/indoor optical wireless communications. Compared with long-reach communications, the ML techniques used in short-reach communications have more stringent complexity and cost requirements, and also need to be more sensitive. In this paper, a comprehensive review of various ML methods and their applications in short-reach optical communications are presented and discussed, focusing on existing and potential advantages, limitations and prospective trends.

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Section: Introductionmentioning

confidence: 99%

“…On the other hand, ML algorithms can learn from historical data without the need of accurate analytical framework. As a result, we have also seen several attempts to apply ML methods for the MFI in short-reach optical communication networks [51,52,[67][68][69].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Applications for Short Reach Optical Communication

et al. 2022

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“…To improve the functionality of the system, various neural networks are used to identify MFs and monitor OSNR simultaneously. In [16][17][18], random forest, Gaussian process regression, support vector machine, and neural network, etc., were used for modulation format recognition and transmission quality monitoring. Zhang et al proposed a cascaded neural network based on transfer learning to simultaneously identify the modulation formats and monitor the OSNR values [19] .…”

Section: Introductionmentioning

confidence: 99%

Joint Modulation Format Identification and Optical Signal-to-Noise Ratio Monitoring Based on Ternary Neural Networks

Zhou

Chen

et al. 2022

IEEE Access

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Modulation format identification (MFI) and optical signal-to-noise ratio (OSNR) monitoring are essential for elastic optical networks. A method for joint modulation format identification and OSNR monitoring based on ternary neural networks was proposed. Further, a ternary neural network was established, and the constellation images after constant modulus algorithm (CMA) equalization were used as input features. Four commonly used modulation formats were distinguished, including dual-polarization (DP)-QPSK, 8QAM, 16QAM, and 64QAM. A 32G baud simulation system was constructed, and the results were analyzed. First, we investigated the influence of resolution and sample length on the identification accuracy. The values 32 and 7000 were selected for the two parameters, respectively, based on the balance between resource consumption and accuracy. Then, we compared the performance of the binary neural network (BNN), ternary neural network (TNN), and full-precision neural network (FNN). The results indicate that the memory consumption and extraction time of the TNN are similar to those of the BNN, and the accuracy is further improved. Moreover, the robustness of the system was analyzed. The results validate that the proposed method can tolerate fiber nonlinearity (from 500 km to 1500 km). Finally, an experiment was conducted to prove the practicality of the proposed method.INDEDX TERMS Modulation format identification (MFI), optical signal-to-noise ratio (OSNR) monitoring, ternary neural network (TNN), optical fiber communication.

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Modulation Format Identification and Transmission Quality Monitoring for Link Establishment in Optical Network Using Machine Learning Techniques

Abstract: We propose and experimentally demonstrate a novel cost-effective and distributed optical performance monitor by employing Gaussian process regression for OSNR monitoring and support vector machine for modulation format identification simultaneously in optical network link establishment.

Cited by 2 publications

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Machine Learning Applications for Short Reach Optical Communication

Machine Learning Applications for Short Reach Optical Communication

Joint Modulation Format Identification and Optical Signal-to-Noise Ratio Monitoring Based on Ternary Neural Networks

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