Modulation format identification using sparse asynchronous amplitude histograms

Zhang, Shuting; Peng, Yingqin; Shu, Qi; Li, Zhaohui

doi:10.1364/sppcom.2015.spm4e.3

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…In addition, the parameters mentioned before (e.g., OSNR), MFI is an important parameter that needs to be monitored during the OPM implementation. Most of the non-machine learning technologies for MFI, such as AAH [86] and asynchronous delayed tap sampling (ADTP) [87], require additional hardware components, which can significantly increase the complexity and cost of implementation. Since neural networks can perform automatic feature extraction and conversion of the channel information required by MFI, the neural networks such as ANN, CNN, and RNN are also good candidates for MFI.…”

Section: Machine Learning For Opm and Mfi In Short Reach Optical Comm...mentioning

confidence: 99%

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: Machine Learning For Opm and Mfi In Short Reach Optical Comm...mentioning

confidence: 99%

Machine Learning Applications for Short Reach Optical Communication

et al. 2022

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“…This modification increased the MFI accuracy by more than 99%. Similarly, AAH has been used in [160], [162] for MFI with ANN classifier optimized by genetic algorithm (GA). The results showed same MFI accuracy as obtained in [159], with few number of neurons and hidden layers.…”

Section: B Ml-based Techniques For Opm and Mfimentioning

confidence: 99%

Machine Learning Techniques for Optical Performance Monitoring and Modulation Format Identification: A Survey

Saif

Esmail

Ragheb

et al. 2020

IEEE Commun. Surv. Tutorials

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The trade-off between more user bandwidth and quality of service requirements introduces unprecedented challenges to the next generation smart optical networks. In this regard, the use of optical performance monitoring (OPM) and modulation format identification (MFI) techniques becomes a common need to enable the development of next-generation autonomous optical networks, with ultra-low latency and selfadaptability. Recently, machine learning (ML)-based techniques have emerged as a vital solution to many challenging aspects of OPM and MFI in terms of reliability, quality, and implementation efficiency. This paper surveys ML-based OPM and MFI techniques proposed in the literature. First, we address the key advantages of employing ML algorithms in optical networks. Then, we review the main optical impairments and modulation formats being monitored and classified, respectively, using ML algorithms. Additionally, we discuss the current status of optical networks in terms of MFI and OPM. This includes standards, monitoring parameters, and the available commercial products with their limitations. Second, we provide a comprehensive review of the available ML-based techniques for MFI, OPM, and joint MFI/OPM, describing their performance, advantages, and limitations. Third, we give an overview of the exiting ML-based OPM and MFI techniques for the emerging optical networks such as the new fiber-based networks that use future space division multiplexing techniques (e.g. few-mode fiber), the hybrid radioover-fiber networks, and the free space optical networks. Finally, we discuss the open issues, potential future research directions, and recommendations for the potential implementation of MLbased OPM and MFI techniques. Some lessons learned are presented after each section throughout the paper to help the reader identifying the gaps, weaknesses, and strengths in this field.

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