Experimental demonstration of joint OSNR monitoring and modulation format identification using asynchronous single channel sampling

Khan, Faisal Nadeem; Chen, Yu; Tan, Ming; Al-Arashi, Waled Hussein; Lau, Alan Pak Tao; Lu, Chao

doi:10.1364/oe.23.030337

Cited by 39 publications

(16 citation statements)

References 13 publications

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“…For joint OPM and BR-MFI, a PCA-based pattern recognition on asynchronous delay-tap plot (ADTP) method was proposed [6]. Likewise, the same method is proposed for joint OSNR monitoring and MFI [7]. The first step of PCA-based pattern recognition algorithm is to extract fixed size feature vectors of the given image and all images in the reference dataset.…”

Section: Joint Optical Performance Monitoring and Modulation Format/bit-rate Identification By Cnn-based Multi-task Learning 1 Introductimentioning

confidence: 99%

Joint Optical Performance Monitoring and Modulation Format/Bit-Rate Identification by CNN-Based Multi-Task Learning

Fan

Xie²,

Ren

et al. 2018

IEEE Photonics J.

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A novel technique is proposed for joint multi-impairment optical performance monitoring (OPM) with bit-rate and modulation format identification (BR-MFI) in next-generation heterogeneous optic communication networks by convolution neural network (CNN)-based deep multi-task learning (MTL) on asynchronous delay-tap sampling phase portraits. Instead of treating the monitoring and identification tasks as separate problems, a novel MTL technique is used to joint optimization of them utilizing the ability of feature extraction and feature sharing. Compared with principal component analysis-based pattern recognition algorithm, CNN-based MTL achieves the better accuracies and has a shorter processing time (∼56 ms). The combination signals of three modulation formats and two bit rates under various impairments are used in numerical simulation. For OPM, the results show monitoring of optical signal-to-noise ratio, chromatic dispersion, and differential group delay with rootmean-square error of 0.73 dB, 1.34 ps/nm, and 0.47 ps, respectively. Similarly, for BR-MFI, even in the case of limited training data, 100% accuracies can be achieved. Additionally, the effects of training data size, task weights, and model structure on CNN-based MTL performance are comprehensively studied. The proposed technique can intelligently analyze the signals of future heterogeneous optic communication networks, and the analysis results are helpful for better management of optical networks.

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Section: Joint Optical Performance Monitoring and Modulation Format/bit-rate Identification By Cnn-based Multi-task Learning 1 Introductimentioning

confidence: 99%

Joint Optical Performance Monitoring and Modulation Format/Bit-Rate Identification by CNN-Based Multi-Task Learning

Fan

Xie²,

Ren

et al. 2018

IEEE Photonics J.

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“…Instead of using two samplers, as in [191] which increases the cost, the authors in [192] proposed MFI and OSNR monitoring technique using PCA in conjunction with the asynchronous single channel sampling (ASCS). ASCS is similar to ADTS but it uses single sampler rather than two samplers.…”

Section: Other Mfi-based Time Domain Features Extraction Techniquesmentioning

confidence: 99%

“…By virtue of the discussion previously presented in Sections IV and V, we observe that most of the features extracted from the time domain signals can be classified according to their sampling technique, either synchronous or asynchronous. Asynchronous features are often used such as the reconstruction eye diagram using the chirp-z conversion software synchronization algorithm [145], AAHs [148], [159], [162], ADTSs [40], [150], [152], [191], [195], ASCS [192], IQH [156], [205], and asynchronous constellation diagram [155]. Most of these features have been extracted using low cost direct detection acquisition systems, making them attractive for intermediate nodes in the optical networks.…”

Section: B Features Utilized 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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“…In recent years, several machine learning-based modulation format identification (MFI) techniques have been proposed both in digital coherent and directly detected receivers [13][14][15][16][17][18][19][20][21][22] for optical communications systems because of their excellent learning ability from data, which can avoid the requirement of pre-information. Khan proposed a deep machine learning method to identify three modulation formats at an accuracy of 100% in a wide optical signal-to-noise ratio range [13].…”

Section: Introductionmentioning

confidence: 99%

A Simple Joint Modulation Format Identification and OSNR Monitoring Scheme for IMDD OOFDM Transceivers Using K-Nearest Neighbor Algorithm

et al. 2019

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Featured Application: This paper provides a feasible method for modulation format identification and OSNR monitoring. The application of KNN reduces complexity.Abstract: In this study, a joint modulation format identification and optical signal-to-noise ratio (OSNR) monitoring algorithm is proposed and experimentally demonstrated using the k-nearest neighbor algorithm for intensity modulation and direct detection (IMDD) orthogonal frequency division multiplexing (OFDM) systems. A modified amplitude histogram of received signal is employed to serve as the classification feature to simplify the computation. Experimental results show that five common quadrature amplitude modulation (QAM) modulation formats, including 4-QAM, 16-QAM, 32-QAM, 64-QAM and 128-QAM, can be identified under 100% accurate estimation at the received optical power of −11 dBm. Robustness of the proposed scheme to constellation rotation is also experimentally assessed. At the same time, system OSNR monitoring also can be achieved and the average prediction mean square error (MSE) is 0.69 dB 2 , which is similar to that using an artificial neural network. Computational complexity assessment demonstrated that similar performance but less computing resource consumption can be achieved by using the proposed scheme rather than the artificial neural network-based scheme.

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Experimental demonstration of joint OSNR monitoring and modulation format identification using asynchronous single channel sampling

Cited by 39 publications

References 13 publications

Joint Optical Performance Monitoring and Modulation Format/Bit-Rate Identification by CNN-Based Multi-Task Learning

Joint Optical Performance Monitoring and Modulation Format/Bit-Rate Identification by CNN-Based Multi-Task Learning

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

A Simple Joint Modulation Format Identification and OSNR Monitoring Scheme for IMDD OOFDM Transceivers Using K-Nearest Neighbor Algorithm

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