Identifying modulation formats through 2D Stokes planes with deep neural networks

Zhang, Wenbo; Zhu, Dingcheng; He, Zihang; Zhang, Nannan; Zhang, Xiaoguang; Li, Yong

doi:10.1364/oe.26.023507

Cited by 44 publications

(21 citation statements)

References 14 publications

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“…In this case, especially when OSNR is relatively low, MFI schemes may not obtain a reliable performance. In [20], we proposed a CNN-based method by projecting constellations in the Stokes space onto the Stokes plane, which can overcome phase noise and frequency offset. In detail, the received PDM signals can be mapped into Stokes space using the formula and (s 1 , s 3 ), then three images for the MFI scheme can be generated.…”

Section: B Stokes Mapping and Image Generationmentioning

confidence: 99%

“…Additionally, the identification rate will be even worse if PS-QAM signals are added. In [20], we proposed a high-performance MFI scheme by successfully using lightweight convolutional neural networks (CNNs). By changing the received signals into Stokes space and projection constellations in Stokes space onto three coordinate planes, the CNN-based MFI scheme can achieve very high accuracy, even if the OSNR is very low.…”

Section: Introductionmentioning

confidence: 99%

“…By changing the received signals into Stokes space and projection constellations in Stokes space onto three coordinate planes, the CNN-based MFI scheme can achieve very high accuracy, even if the OSNR is very low. It is also reasonable to design the MFI scheme following a similar step introduced in [20].…”

Section: Introductionmentioning

confidence: 99%

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Identifying Probabilistically Shaped Modulation Formats Through 2D Stokes Planes With Two-Stage Deep Neural Networks

et al. 2020

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A lightweight two-stage convolutional (deep) neural network (CNN) based modulation format identification (MFI) scheme is proposed and demonstrated for the polarization domain multiplexing (PDM) fiber communication system with probabilistically shaped (PS) modulation formats. The scheme is tested on a PDM system at a symbol rate of 28 GBaud. Six probabilistically shaped (PS) modulation formats (of 3 bit/symbol PS-16QAM, PS-32QAM, and PS-64QAM, of 4 bit/symbol PS-32QAM and PS-64QAM, and of 5 bit/symbol PS-64QAM) along with six standard modulation formats (BPSK, QPSK, 8PSK and three uniformly shaped (US) QAM: US-16QAM, US-32QAM and US-64QAM) are identified by the trained CNN. By taking advantage of computer vision, the results show that the proposed scheme can provide very high accuracy and significantly improve the identification performance over the existing techniques. The influences of the learning rate of the CNN are also discussed. INDEX TERMS Optical fiber communication, modulation format identification, convolutional neural networks.

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Section: B Stokes Mapping and Image Generationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Identifying Probabilistically Shaped Modulation Formats Through 2D Stokes Planes With Two-Stage Deep Neural Networks

et al. 2020

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“…Machine learning has been successfully applied to a wide variety of problems in the context of telecommunication networks, mainly in wireless networks, ranging from opportunistic spectrum access, to channel estimation and signal detection in orthogonal-frequency division multiplexing (OFDM) to multiple-input-multipleoutput (MIMO) communications. Many techniques within the previous categories can be addressed at the physical layer of optical transmission for optical performance monitoring (OPM) [33][34][35][36][37][38][39][40], modulation format recognition (MFR) [41], or nonlinearity mitigation. Ideally, OPM should be implemented just with a single photodiode and machine learning algorithms that can learn the mapping between the detected signal and optical fiber channel parameters and finally predict optical fiber channel parameters from energy constellation diagrams or power eye diagrams.…”

Section: Machine Learning (Ml) Techniquesmentioning

confidence: 99%

Machine Learning Techniques to Mitigate Nonlinear Phase Noise in Moderate Baud Rate Optical Communication Systems

Fernández¹,

Soto²,

González³

et al. 2020

Intelligent System and Computing

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Nonlinear phase noise (NLPN) is the most common impairment that degrades the performance of radio-over-fiber networks. The effect of NLPN in the constellation diagram consists of a shape distortion of symbols that increases the symbol error rate due to symbol overlapping when using a conventional demodulation grid. Symbol shape characterization was obtained experimentally at a moderate baud rate (250 MBd) for constellations impaired by phase noise due to a mismatch between the optical carrier and the transmitted radio frequency signal. Machine learning algorithms have become a powerful tool to perform monitoring and to identify and mitigate distortions introduced in both the electrical and optical domains. Clustering-based demodulation assisted with Voronoi contours enables the definition of non-Gaussian boundaries to provide flexible demodulation of 16-QAM and 4+12 PSK modulation formats. Phase-offset and in-phase and quadrature imbalance may be detected on the received constellation and compensated by applying thresholding boundaries obtained from impairment characterization through statistical analysis. Experimental results show increased tolerance to the optical signal-to-noise ratio (OSNR) obtained from clustering methods based on k-means and fuzzy c-means Gustafson-Kessel algorithms. Improvements of 3.2 dB for 16-QAM, and 1.4 dB for 4+12 PSK in the OSNR scale as a function of the bit error rate are obtained without requiring additional compensation algorithms.

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“…Compared with the traditional machine learning (ML) methods, DL has the significant advantages of self-learning and automatic feature extraction [6]. Naturally, with the purpose of improving the monitoring accuracy, more and more DL technologies are used in OPM [7] as well as BR-MFI [8], [9]. Moreover, some work even realize the BR-MFI and OPM simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Credibility of the Optical Performance Monitor With Adversarial Training

Fan

Su²,

Dong³

et al. 2020

IEEE Access

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The existing optical performance monitoring (OPM) scheme based on deep neural network has no selection capability of the input data. They always accept and process all, which may result in serious monitoring errors and reduce the credibility of the monitoring system. Because the transmitted data in the future heterogeneous fiber-optic networks are diverse, and it's likely to exceed the scope of the monitoring system. We propose an unsupervised generative adversarial network (GAN) as the judgement module in the new OPM framework to select the legal data within the scope of the monitoring system. The generator consists of encoder-decoder-encoder (EDE) sub-network, jointly learns the image and latent feature distribution of the legal data. And the training data for the network in the new added judgement module is the same as the OPM analyzer network's, therefore, no extra data are collected, which is low-cost. In the simulation, four modulation formats under two bit-rates are taken into account to verify the model performance in the judgement module. When 60 Gbps 64QAM signal is selected as illegal data, the max value of the area under the curve (AUC) is 0.942. The judgement time for single image is about 12 ms. Moreover, the influence of the task weights and the latent feature shape on the judgement performance are investigated. The new added judgement module largely increases the credibility and safety of the existing OPM scheme. INDEX TERMS Optical performance monitoring (OPM), generative adversarial network (GAN).

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Identifying modulation formats through 2D Stokes planes with deep neural networks

Cited by 44 publications

References 14 publications

Identifying Probabilistically Shaped Modulation Formats Through 2D Stokes Planes With Two-Stage Deep Neural Networks

Identifying Probabilistically Shaped Modulation Formats Through 2D Stokes Planes With Two-Stage Deep Neural Networks

Machine Learning Techniques to Mitigate Nonlinear Phase Noise in Moderate Baud Rate Optical Communication Systems

Enhancing the Credibility of the Optical Performance Monitor With Adversarial Training

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