Modulation format identification based on logistics regression for high-speed coherent optical communication system

In coherent optical communication systems, where multiple modulation formats are mixed and variable, the correct identification of signal modulation formats provides the foundation for subsequent performance improvement using digital algorithms. A modulation format identification (MFI) scheme based on signal constellation diagrams and support vector machine (SVM) is proposed. Firstly, the signal constellation diagrams are divided by the fractal dimension of the weighted linear least squares (WLS-FD) algorithm, and the fractal dimension (FD) in each region is calculated, which is regarded as one of the image features. Then, the feature values of the image in different directions are extracted by the gray-level co-occurrence matrix (GLCM), and their mean and variance are calculated, which is regarded as another feature. Finally, the two features are input into the modulation format classifier constructed by the SVM to achieve MFI in coherent optical communication systems. To verify the feasibility and superiority of the scheme, we compare it with the MFI scheme based on higher-order statistical (HOS) features, GLCM features, and FD features, respectively. Further, we built a 30 GBaud coherent optical communication system with fiber lengths of 80 km and 120 km, where the optical signal-to-noise ratio (OSNR) ranges from 0 dB to 30 dB. The proposed MFI scheme identifies seven modulation formats: QPSK, 8QAM, 16QAM, 32QAM, 64QAM, 128QAM, and 256QAM. The results show that compared with the other three schemes, our proposed scheme has a better identification accuracy at low OSNR. In addition, the identification accuracy of this scheme can reach 100% when the OSNR ≥ 10 dB.

In this study, we develop an intelligent constellation diagram monitoring method for fiber optic communications based on a small sample learning network. The method is designed to identify the modulation format, estimate the optical signal-to-noise ratio (OSNR), and predict the fiber transmission distance by analyzing a small amount of constellation diagram data. The model was trained using the C-way-K-shot strategy with the Adam optimization algorithm, with a learning rate of 0.001. In terms of modulation format identification, the model was tested under different OSNR conditions, and the results showed that under low, medium, and high OSNR conditions, the 5-way-3-shot task models achieved average accuracies over 10 iterations of 95.73%, 98.35%, and 97.86%, respectively. In contrast, for the 5-way-5-shot task, the accuracies increased to 98.38%, 99.23%, and 98.74%, respectively. This indicates that, even under low OSNR conditions, the small-sample learning network can achieve a high modulation format recognition rate, demonstrating the model’s fast adaptability and high accuracy.Meanwhile, the OSNR estimation in low-order modulation formats achieves a 100% recognition rate, and for high-order modulation formats, the estimation also performs well. The small-sample learning network demonstrates good performance even with a limited data sample size. The recognition rate of transmission distance estimation for low-order modulation formats, such as BPSK and QPSK, performs well under low, medium, and high OSNR conditions. However, performance in recognizing higher-order modulation formats, such as 64QAM, is poor, highlighting both the adaptability and limitations of the model when dealing with different modulation formats. This study demonstrates the potential of small-sample learning networks for intelligent constellation diagram monitoring in fiber optic communications, especially in terms of their high accuracy and robustness in modulation format identification, OSNR estimation, and fiber link transmission distance prediction. The method effectively utilizes a small sample size to achieve fast and accurate monitoring, thus providing effective technical support for the intelligent management and maintenance of fiber optic communication systems.

Joint modulation format identification and OSNR estimation method based on trajectory information analysis

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.

This paper presents a novel joint optical performance monitoring (OPM) scheme for multiple spatial modes in mode division multiplexing elastic optical network (MDM-EON) systems, which integrates the optimal Radon transform (ORT) image with the improved multi-task multi-input multi-output residual neural network (IMT-MIMO-ResNet) model. The ORT images effectively enhance the differentiation among various parameters across different spatial modes. Additionally, based on the output characteristics and intrinsic correlations among spatial modes of the MDM-EON system, we design the IMT-MIMO-ResNet model. The proposed scheme overcomes the limitations of existing approaches, where a single OPM device can monitor only one spatial mode. It enables simultaneous monitoring of bit rates (BRs), modulation formats (MFs), mode coupling (MC) intensities, and optical signal-to-noise ratio (OSNR) values for each MF across five spatial modes. Moreover, the scheme demonstrates favorable robustness under conditions with large chromatic dispersion, large differential group delay, and strong fiber nonlinear effects. Both simulation and experimental results show that the accuracy of baud rate identification (BRI) and modulation format identification (MFI) for each spatial mode reaches 100%, and the average mean absolute errors (MAEs) for OSNR values and MC intensities corresponding to each MF are below 0.4 dB and 0.010, respectively. The computational complexity of the proposed scheme, evaluated using floating-point operations (FLOPs) and parameters (Params), is approximately 1/7 of that required by a typical OPM scheme.

Optical signal to noise ratio (OSNR) is a critical performance parameter of an optical fiber communication system. A method for OSNR monitoring based on coherent optical sampling technology is presented. The estimated error of a 10-GBd QPSK signal is <1 dB with the actual OSNR in range of 12 to 30 dB. The influence of uneven spectrum of a local oscillator on the measured accuracy is investigated, and a compensated algorithm for such unevenness is provided. Our method has the advantage of lower complexity and can be extended to higher symbol rate communication system requiring no high bandwidth ADC hardware.

Acoustic measurement of physical properties

PMD mitigation through interleaving LDPC codes with polarization scramblers

In this work, a prior-training-free and low-complexity modulation format identification (MFI) scheme, based on amplitude histogram distributions, was proposed and demonstrated, both numerically and experimentally, for autonomous digital coherent receivers. In the proposed scheme, after having performed power normalization, incoming polarization division multiplexed (PDM) signals were classified into QPSK, 8QAM, 16QAM, 32QAM and 64QAM signals, according to their ratios. Ratios were defined according to specific features of their amplitude histograms. The proposed MFI scheme used only amplitude information. As such, it was insensitive to carrier phase noise. Furthermore, the proposed scheme did not require any prior information, such as optical signal-to-noise ratio (OSNR). The performance of the proposed MFI scheme was numerically verified using 28GBaud PDM-QPSK/-8QAM/-16QAM/-32QAM/-64QAM signals. The numerical simulation results showed that the proposed scheme was able achieve a 100% correct identification rate for all five modulation formats when their OSNR values were higher than the thresholds corresponding to the 20% FEC correcting bit error rate (BER) of 2.4 × 10−2. To further explore the effectiveness of the proposed MFI scheme, proof-of-concept experiments in 28GBaud PDM-QPSK/-8QAM/-16QAM, and 21.5GBaud PDM-32QAM transmission systems were also undertaken, which showed that the proposed scheme as robust against fiber nonlinearities. To explore the scheme’s feasibility for use in practical transmission systems, the computational complexity analysis of the proposed scheme was conducted. It showed that, compared with relevant MFI schemes, the proposed MFI scheme was able to significantly reduce the computational complexity.

For enhancing the security of coherent optical communication systems, an optical transmitter identification method based on constellation diagrams is proposed. Addressing the limitations of existing optical transmitter identification methods in adapting to higher-order modulation formats, the constellation diagram is employed as the optical transmitter fingerprint. Different optical transmitters can be distinguished by the amplitude features and phase features in the constellation diagram. Four CNN networks are applied for extracting these features to classify optical transmitters, including CX-LeNet, CX-AlexNet, VGG-16 and ResNet-18. Recognition performance of a network is evaluated with the help of recognition accuracy and PR curves. The employment of the grad-CAM explainer reflects the attention of the network. In the back-to-back system, the accuracy of classification decreases with the increasing symbol rate and increases with the increasing number of symbols. In the 100 km coherent optical communication system with the symbol rate of 25 GBaud, the accuracy of identifying 6 optical transmitters transmitting 16QAM signals with the VGG-16 network is 100%. Further simulation proves that the proposed method is still feasible in high-speed and long-distance coherent optical communication systems. Effective detection of unauthorized samples proves the ability of the proposed method to enhance the security of coherent optical communication systems.

Differential phase-shift keying (DPSK) has been widely implemented and developed in high-speed optical communication systems. The low error rate detection at high access rate is one of the considerable issues in practical engineering application. Balanced detection based on fiber Mach-Zehnder delay interferometer (MZDI) is the typical optical DPSK signal detecting method. It requires that the free spectrum range (FSR) of the MZDI equals the reciprocal of symbol period of the DPSK signal. For the reasons of ambient temperature variation and nonlinear phase noise, a dynamic frequency offset always exists between the FSR and the reciprocal of symbol period. That may introduce some optical signal-to-noise ratio (OSNR) costs and fault detections. Therefore, it is significant to inhibit the frequency offset on DPSK detection. In this paper, firstly, we discuss the effects of frequency offset on DPSK detection, and realize the conclusion that frequency offset is virtually equivalent to an additional phase difference between adjacent symbols. Secondly, through simulation, we analyze the feasibility of DPSK detection in the presence of a definite range of frequency offset, and present the quantitative computation of effective coverage, duty cycle, and optimal sampling time of symbol interference. Some issues which should be considered in practical implementation are also discussed. Finally, according to the relationship among phase difference, temperature and voltage, we propose a phase difference compensation scheme which can automatically adjust the voltage for optimal detections, and dynamically track the changing of ambient temperature and nonlinear phase noise. Furthermore, we ascertain the performance of the voltage requested for implementing the scheme. The scheme can be also developed to quadrature phase-shift keying (QPSK) and differential QPSK (DQPSK) modulation situations.

As human society to the information in the process of moving and growing demand for bandwidth communications capacity, the optical of new modulation formats increasingly attention and quickly play an important role in optical communications. How can the system bit error rate within a certain degree of stability while still maintaining high-speed long-distance dispersal system, has been a popular issue is the optical communications industry. Starting from the optical modulation format herein, the generation process of the system introduced various optical signal modulation format, the optical signal through the optical fiber was studied and the performance of the simulation, on the basis of the design of advanced optical modulation formats in an optical fiber communication system .

In this paper, asynchronous complex histogram (ACH)-based multi-task artificial neural networks (MT-ANNs), are proposed to realize modulation format identification (MFI), optical signal-to-noise ratio (OSNR) estimation and fiber nonlinear (NL) noise power estimation simultaneously for coherent optical communication. Optical performance monitoring (OPM) is demonstrated with polarization mode multiplexing (PDM), 16 quadrature amplitude modulation (QAM), PDM-32QAM, as well as PDM-star 16QAM (S-16QAM) for the first time. The range of launched power is −3 to −2 dBm with a fiber link of 160–1600 km. Then, the accuracy of MFI reaches 100%. The average root mean square error (RMSE) of OSNR estimation can reach 0.37 dB. The average RMSE of NL noise power estimation can reach 0.25 dB. The results show that the monitoring scheme is robust to the increase of fiber length, and the solution can monitor more optical network parameters with better performance and fewer training data, simultaneously. The proposed ACH MT-ANN has certain reference significance for the future long-haul coherent OPM system.

A modulation format identification (MFI) scheme based on multi-dimensional amplitude features is proposed for elastic optical networks. According to the multi-dimensional amplitude features, incoming polarization division multiplexed (PDM) signals can be identified as QPSK, 8QAM, 16QAM, 32QAM, 64QAM and 128QAM signals using the k-nearest neighbors (KNNs) algorithm in the digital coherent receivers. The proposed scheme does not require any prior training or optical signal-to-noise ratio (OSNR) information. The performance of the proposed MFI scheme is verified based on numerical simulations with 28GBaud PDM-QPSK/-8QAM/-16QAM/-32QAM/-64QAM/-128QAM signals. The results show that the proposed scheme can achieve 100% of the correct MFI rate for all six modulation formats when the OSNR values are greater than their thresholds corresponding to the 20% forward error correction (FEC) related to a BER of 2.4 × 10−2. Meanwhile, the effects of residual chromatic dispersion, polarization mode dispersion and fiber nonlinearities on the proposed scheme are also explored. Finally, the computational complexity of the proposed scheme is analyzed, which is compared with relevant MFI schemes. The work indicates that the proposed technique could be regarded as a good candidate for identifying modulation formats up to 128QAM.

OSNR monitoring technique based on multi-order statistical moment method and correlation function for PM-16QAM in presence of fiber nonlinearities