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

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.

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.

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.

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.

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.

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.

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) method based on the nonlinear power transformation via logistics regression is adopted for coherent optical receives systems. The amplitude variance, fourth power transformation and fast Fourier transform of input signals are utilized for special features extraction in our work. Five typical optical modulation formats (i.e.,16/32/64QAM and Q/8PSK) with the transmission rate of 28 GBaud are numerically simulated to demonstrate the feasibility. The simulation results show that our method has great performance even under low optical signal noise ratio (OSNR). Compared with the MFI algorithm based on Stokes space and asynchronous delay tapped sampling, our MFI algorithm requires less time to achieve similar performance of optical receive systems. Especially, this method exhibits tolerances to the laser linewidth and nonlinearity.

Cost-effective OSNR monitoring with large chromatic dispersion tolerance using random forest for intermediate nodes

The WiMax (Worldwide Interoperability for Microwave Access) is a technology in<strong> </strong>broadband wireless access, which employs OFDM (Orthogonal Frequency Division Multiplexing) as an alternative transmission to enable high speed data in communication system. This research aim is to analyze the performance system of the OFDM-Based WiMAX, which used the cyclic prefix. The model was designed in four schemes of simulation method, the BPSK (Binary Phase Shift Keying, QPSK (Quadrature Phase Shift Keying), 16 QAM (Quadrature Amplitude Modulation) and 64 QAM. Each scheme was investigated BER (Bit Error Rate) on AWGN (Additive White Gaussian Noise) channel and multipath Rayleigh fading channel, which had applied the cyclic prefix. By simulation of the cyclic prefix was produced the modulation measurement of the BPSK, QPSK, 16 and 64 QAM. The performance result of Eb/No 15 dB was obtained the BER of BPSK and QPSK of 1.11E-11, the BER of 16 QAM and 64 QAM of 8.69E-06 and 0.00333 respectively. Those results indicated much smaller BER value than EbNo 0 dB which BPSK and QPSK of 1 BER, 1.5 and 1.75 BER for 16 QAM and 64 QAM respectively. In conclusion, a higher value of EbNo, hence the BER value would be lower.

Acoustic measurement of physical properties

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

Transmission and reception of PDM dual-subcarrier coherent 16QAM-OFDM signals

A novel in-band OSNR monitoring technique based on fractional Fourier transform of LFM signal

There is a need, for high speed optical communication networks, in the monitoring process, to determine the modulation format type of a received signal. In this paper, we present a new achievement of modulation format recognition technique, where we proposed the use of wavelet transform of the detected signal in conjunction with the artificial neural network (ANN) algorithm. Besides, wavelet transform is one of the most popular candidates of the time-frequency transformations, where the wavelets are generated from a basic wavelet function by dilations and translations. We proved that this technique is capable of recognizing the multi-carriers modulation scheme with high accuracy under different transmission impairments such as chromatic dispersion (CD), differential group delay (DGD) and accumulated amplified spontaneous emission (ASE) noise with different ranges. Both the theoretical analysis and the simulation results showed that the wavelet transform not only can be used for modulation identification of optical communication signals, but also has a better classification accuracies under appropriate OSNR (optical signal-to-noise ratio) values.