Modulation Format Identification Based on Signal Constellation Diagrams and Support Vector Machine

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.

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.

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.

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

Deep learning-based nonlinear phase shift estimation in coherent optical communication systems

The high speed rotation of state of polarization (RSOP) is a key issue for 16 quadrature amplitude modulation (QAM) coherent optical polarization division multiplexing (PDM) communication systems in strong lightning weather. To reduce the bit error rate (BER) caused by the high speed of RSOP, the historical information is first proposed to correct the demodulation process in coherent optical communication systems. By adding the time reverse error term into the cost function, a new equalization algorithm is proposed for coherent optical communication systems. Compared with the conventional Multi-modulus Algorithm (MMA), simulation results show that the average BER and steady-state error (SSE) of the new algorithm is reduced by 33.47% and 4.08%, respectively. Moreover, the average tolerance of RSOP of the new algorithm is increased by 28.57% compared with the MMA in 16 QAM coherent optical PDM communication systems.

Theory of Optical Communications. Fiber Devices for Optical Communication Systems. Integrated Devices for Optical Communication Systems. Direct Detection Optical Communication Systems. Coherent Systems: Structure and Ideal Performance. Performance Degradation Sources in Coherent Optical Systems. Multilevel Coherent Optical Systems. Multichannel Optical Systems. Present--Day Systems Implementations. Appendices. Index.

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.

We propose a method of modulation format identification based on compressed sensing using a high-order cyclic cumulant combined with a binary tree classifier. Through computing the fourth-order cyclic cumulant of the pretreated band signal, which is obtained by compressed sensing with the sampling rate much less than the Nyquist sampling value, the feature vector for classification is extracted. Simulations are carried out in the optical coherent fiber communication system with different modulation formats of multiple phase-shift keying and multiple quadrature amplitude modulation. The results indicate that this method can identify these modulation formats correctly and efficiently. Meanwhile, the proposed method is insensitive to laser phase noise and signal noise.

Acoustic measurement of physical properties

The interplay between the stochastic intensity fluctuation of Raman pump laser and cross-phase modulation (XPM) effect in transmission optical fiber leads to additional phase noise, namely, relative phase noise (RPN) of signal in multi-level modulated coherent optical communication system. Both theoretical analysis and quantitative simulation have been performed to investigate the characteristics and impact of RPN. Being low-pass in nature, RPN is different from XPM induced phase noise in PSK/OOK hybrid system, and has not been considered yet. The noise power of RPN can accumulate incoherently along transmission links. With a proper signal model, we study the impact of RPN to the coherent optical communication system through Monte Carlo simulation. RPN will cause more cycle slips in Viterbi-and-Viterbi (V-V) phase estimation (PE), and the quantitative analysis of cycle slip probability is carried out. When using sliding window V-V without any optimization, the Q factor penalty of RPN on DQPSK signal can be as large as around 5 dB in strong RPN condition. However, it can be reduced by over 3 dB when using an optimal block size or optimal averaging weights.

Coherent optical communication systems have high receiver sensitivities, high spectral efficiencies, and high-capacity information transmission, which are widely used in free-space optical communications. However, atmospheric turbulence affects the power budget of coherent receiving systems. Diversity can effectively suppress atmospheric turbulence, but relative phase differences caused by phase asynchrony degrade the performances of diversity systems. Hence, spatial diversity reception based on optimal branch block phase correction is proposed herein and verified through simulations and experiments to improve diversity gain and reduce the complexity and outage probability of diversity systems effectively. This scheme is promising for application to high-speed low Earth orbit satellite-to-ground communications.

We propose a joint algorithm for nonlinear channel equalization in coherent optical communication system. We have experimentally demonstrated its performance and complexity, and analyzed the data pseudo-random problem.