Long Short-Term Memory Neural Network (LSTM-NN) Enabled Accurate Optical Signal-to-Noise Ratio (OSNR) Monitoring

Wang, Chunxiao; Fu, Songnian; Xiao, Zhou; Tang, Ming; Liu, Deming

doi:10.1109/jlt.2019.2904263

Cited by 41 publications

(14 citation statements)

References 25 publications

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“…In Wang et al (2019b), a Long Short-Term Memory (LSTM) neural network was used to approximate the OSNR without need for manual feature extraction. The four tributary output from the coherent receiver was used as input.…”

Section: Machine Learning Applied To Coherent Detection Systemsmentioning

confidence: 99%

Machine Learning-Aided Optical Performance Monitoring Techniques: A Review

2022

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Future communication systems are faced with increased demand for high capacity, dynamic bandwidth, reliability and heterogeneous traffic. To meet these requirements, networks have become more complex and thus require new design methods and monitoring techniques, as they evolve towards becoming autonomous. Machine learning has come to the forefront in recent years as a promising technology to aid in this evolution. Optical fiber communications can already provide the high capacity required for most applications, however, there is a need for increased scalability and adaptability to changing user demands and link conditions. Accurate performance monitoring is an integral part of this transformation. In this paper, we review optical performance monitoring techniques where machine learning algorithms have been applied. Moreover, since many performance monitoring approaches in the optical domain depend on knowledge of the signal type, we also review work for modulation format recognition and bitrate identification. We additionally briefly introduce a neuromorphic approach as an emerging technique that has only recently been applied to this domain.

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Section: Machine Learning Applied To Coherent Detection Systemsmentioning

confidence: 99%

Machine Learning-Aided Optical Performance Monitoring Techniques: A Review

2022

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“…This method is validated by simulations and experiments. In [64], the long short-term memory (LSTM) neural network is applied to monitor the OSNR with the four-tributary digital outputs. The mean absolute error can be significantly reduced from 0.4 to 0.04 dB compared with other ML algorithms.…”

Section: Ai-based Qot and Impairment Monitoringmentioning

confidence: 99%

AI-Based Modeling and Monitoring Techniques for Future Intelligent Elastic Optical Networks

Liu

Lun

et al. 2020

Applied Sciences

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With the development of 5G technology, high definition video and internet of things, the capacity demand for optical networks has been increasing dramatically. To fulfill the capacity demand, low-margin optical network is attracting attentions. Therefore, planning tools with higher accuracy are needed and accurate models for quality of transmission (QoT) and impairments are the key elements to achieve this. Moreover, since the margin is low, maintaining the reliability of the optical network is also essential and optical performance monitoring (OPM) is desired. With OPM, controllers can adapt the configuration of the physical layer and detect anomalies. However, considering the heterogeneity of the modern optical network, it is difficult to build such accurate modeling and monitoring tools using traditional analytical methods. Fortunately, data-driven artificial intelligence (AI) provides a promising path. In this paper, we firstly discuss the requirements for adopting AI approaches in optical networks. Then, we review various recent progress of AI-based QoT/impairments modeling and monitoring schemes. We categorize these proposed methods by their functions and summarize advantages and challenges of adopting AI methods for these tasks. We discuss the problems remained for deploying AI-based methods to a practical system and present some possible directions for future investigation.

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“…In the literature, Q-factor [11] and BER [12,13] are chosen for the quality of transmission (QoT) estimation, which are typically performed establishing new lightpaths. Besides, OSNR is widely studied for OPM to measure the actual optical signal itself to estimate signal/link quality [14][15][16][17][18][19][20]. Monitoring EVM can extend the functionality of the OPM module, which provides more accurate estimation of the performance compared with the OSNR only.…”

Section: Introductionmentioning

confidence: 99%

“…In this regard, deep learning is widely considered [5,11,[13][14][15][16][17][18][19][20][21][22][23]. It has strong capability to extract features from different signal representations, such as time-domain symbol sequence [14,15], phase portrait [16], frequency-domain transformation [17], constellation diagram in the In-phase/Quadrature (IQ) complex plane [9,18,21], and amplitude histogram (AH) [10,19,20]. Recently we have proposed a fast and accurate EVM estimation scheme based on a convolutional neural network (CNN) [9,10].…”

Section: Introductionmentioning

confidence: 99%

Experimental validation of CNNs versus FFNNs for time- and energy-efficient EVM estimation in coherent optical systems

Fan

Udaļcovs

Natalino

et al. 2021

J. Opt. Commun. Netw.

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Error vector magnitude (EVM) has proven to be one of the optical performance monitoring (OPM) metrics providing the quantitative estimation of the error statistics. However, the EVM estimation efficiency is not fully exploited in terms of complexity and energy consumption. Therefore, in this article, we explore two deep-learning-based EVM estimation schemes. The first scheme exploits convolutional neural networks (CNNs) to extract EVM information from images of constellation diagram in the In-phase/Quadrature (IQ) complex plane or amplitude histograms (AH). The second scheme relies on feedforward neural networks (FFNN) extracting features from a vectorized representation of AHs. In both cases, we use short sequences of 32 Gbaud m-ary quadrature amplitude modulation (mQAM) signals captured before or after a carrier phase recovery (CPR). The impacts of the sequence length, neural network structure, and dataset representation on the EVM estimation accuracy as well as the model training time are thoroughly studied. Furthermore, we validate the performance of the proposed schemes using the experimental implementation of 28 Gbaud 64QAM signals. We achieve a mean absolute estimation error below 0.15% with short signals consisting of only 100 symbols per IQ cluster. Considering the estimation accuracy, the implementation complexity, and the potential energy savings, the proposed CNN-and FFNN-based schemes can be used to perform time-sensitive and accurate EVM estimation for mQAM signal quality monitoring purposes.

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Long Short-Term Memory Neural Network (LSTM-NN) Enabled Accurate Optical Signal-to-Noise Ratio (OSNR) Monitoring

Cited by 41 publications

References 25 publications

Machine Learning-Aided Optical Performance Monitoring Techniques: A Review

Machine Learning-Aided Optical Performance Monitoring Techniques: A Review

AI-Based Modeling and Monitoring Techniques for Future Intelligent Elastic Optical Networks

Experimental validation of CNNs versus FFNNs for time- and energy-efficient EVM estimation in coherent optical systems

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