Modulation Format Identification Research Articles

Modulation Format Identification Method Based on Multi-Feature Input Hybrid Neural Network

Modulation Format Identification Method Based on Multi-Feature Input Hybrid Neural Network

Multi-task metric learning for optical performance monitoring

In our experiments, applying few shot metric learning for optical performance monitoring (OPM), we set the dataset as 16-way-6-shot. Modulation format identification (MFI) was utilized as a classification task, and optical signal-to-noise ratio (OSNR) estimation was used as a regression task for joint analysis. Multi-task metric learning (MML) used the adaptive weights to balance the weights of the three metric functions, six modulation formats (QPSK, 8QAM, 16QAM, 32QAM, 64QAM, 128QAM) are classified correctly with 100 % accuracy after 200 epochs. Furthermore, the lowest mean square error (MSE) of OSNR is 0.431 dB. Then, Ablation experiments compute the corresponding similarity (SIM) for each metric function show that the MSE of MML, SIMLocal+Cosine, SIMCosine+Point, SIMLocal+Point, single-task metric learning (SML) and adaptive multi-task learning (AMTL) is 0.431 dB, 0.572 dB, 0.569 dB, 0.567 dB, 0.637 dB, 1.319 dB, respectively. The proposed model achieves the highest accuracy in MFI and the lowest MSE in OSNR estimation. Finally, when comparing the various metric functions while altering the transmission distance of the optical fiber, it was observed that MML stayed within an acceptable range between 200 km and 800 km. This shows that our algorithm requires only a small number of training samples to create a reasonably good model, offering a new approach to solving problems that arise in optical performance monitoring.

ANN/Random forest based performance monitoring in high-speed short-reach optical interconnections

In this work, we have developed signal quality monitoring approaches in 100/400 Gbit/s short-reach transmission systems, with the application of four advanced modulation formats. In 100G and 400G transmission systems, it is shown that accuracies of 100 % have been achieved in the modulation format identification (MFI), with the use of random forest (RF) and multitask learning-based artificial neural network (MTL-ANN) for the four modulation formats mentioned. Meanwhile, average mean-square errors (MSEs) of the monitored optical signal-to-noise ratio (OSNRs) are less than 0.1 dB. Random forest uses up to 29 adders and 190 comparators, reducing its complexity by two orders of magnitude compared to MTL-ANN.

Deep learning approach for automatic modulation format identification in vehicular visible light communications

Deep learning approach for automatic modulation format identification in vehicular visible light communications

Modulation Format Identification Based on Multi-Dimensional Amplitude Features for Elastic Optical Networks

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.

Combined modulation format identification and optical signal-to-noise ratio monitoring with high accuracy and generalizability based on a proposed fused module few-shot learning algorithm in dynamic coherent optical transmissions

Modulation format identification (MFI) and optical signal-to-noise ratio (OSNR) monitoring are important portions of optical performance monitoring (OPM) for future dynamic optical networks. In this paper, we proposed a fusion module few-shot learning (FMFSL) algorithm as an improvement upon the ordinary few-shot learning algorithms for image recognition with the specialty in adopting a combination of a dilated convolutional group and an asymmetric convolutional group to advance the feature extraction. FMFSL algorithm is applied in MFI and OSNR monitoring in coherent optical communication systems with its performance investigated in both back-to-back and fiber transmission scenarios using small-scale constellation diagrams. The results show that FMFSL algorithm can achieve 100% accuracy in MFI and higher OSNR monitoring accuracy compared to the few-shot learning algorithms Deep Nearest Neighbor Neural Network (DN4) and Prototypical Nets (PN) with 2.14% and 4.28% for 64QAM and 3.38% and 8.06% for 128QAM, respectively, without much increase in time consumption. Furthermore, the trained FMFSL algorithm remains excellent in MFI and OSNR monitoring without retraining while employed in back-to-back transmission scenarios with smaller OSNR intervals and fiber transmission scenarios with different amounts of Kerr nonlinearity, demonstrating its high capabilities in generalization and robustness. FMFSL algorithm provides a potential solution for OPM in future dynamic optical networks as a novel machine learning tool.

A cost-effective joint multi-parameter optical performance monitoring scheme for high baud rate mode division multiplexing system

Inter-symbol interference (ISI), mode coupling (MC) and other impairments present in high baud rate mode division multiplexing (MDM) systems significantly degrade the reliability and accuracy of optical signal transmission. Therefore, performing joint multi-parameter optical performance monitoring (OPM) is crucial to ensure the efficient operation of the system. We propose a scheme that combines asynchronous single channel sampling (ASCS) images with a multi-task residual neural network (MT-ResNet) to enable cost-effective joint OPM at intermediate nodes in a 100 GBaud MDM system. The scheme requires only one photodetector to receive the signal. Based on the distinct characteristics exhibited by ASCS images for different parameters in the MDM system, the MT-ResNet is improved to capture correlations among parameters and learn residual information between inputs and outputs. This enables high-precision and rapid simultaneous implementation of modulation format identification (MFI), MC estimation, optical signal-to-noise ratio (OSNR) estimation, and chromatic dispersion identification (CDI) in the intermediate nodes of a 100 GBaud MDM system. The effectiveness of the proposed scheme is verified using a 100 GBaud QPSK/8QAM/16QAM/32QAM/64QAM MDM simulation transmission system with five spatial modes (LP01/LP11a/LP11b/LP21a/LP21b). The results show that under the influence of different linewidth (LW) of laser and differential mode group delay (DMGD), a 100 % identification success rate can be achieved for five commonly used MFs and eight different cumulative CD values in high baud rate MDM systems, across a wide range of OSNR. Additionally, the mean absolute error (MAE) of OSNR estimation corresponding to the five MFs is 0.22 dB, 0.26 dB, 0.30 dB, 0.32 dB, and 0.36 dB, respectively. The MAE of the eight different MC intensities fluctuate between 0.015 and 0.030. These results fully demonstrate the superior performance of the proposed scheme. Furthermore, the scheme exhibits favorable tolerance to large LWs and fiber nonlinear effects. Moreover, the complexity of the scheme is accurately analyzed through floating-point operations (FLOPs) and parameters (Params), which are much less than existing typical schemes, with specific values of 14.8849 M and 1.3798 M.

Joint modulation format identification and OSNR monitoring based on Stokes vector distribution features for digital coherent optical receivers

Joint modulation format identification and OSNR monitoring based on Stokes vector distribution features for digital coherent optical receivers

Intelligent joint multi-parameter optical performance monitoring scheme based on HT images and MT-ResNet for elastic optical network

In elastic optical network (EON), joint multi-parameter optical performance monitoring (OPM) can effectively manage, diagnose, and reduce operational costs for transmission optical links. In this paper, we propose a novel joint monitoring scheme utilizing Hough transform (HT) images combined with multi-task residual neural network (MT-ResNet) for EON. The scheme can realize baud rate identification (BRI), modulation format identification (MFI), residual chromatic dispersion identification (CDI), optical signal-to-noise ratio (OSNR) and residual differential group delay (DGD) estimation at the same time. The HT image is obtained by preprocessing original constellation diagram, which is a key feature of parameter monitoring for EON signals and presents obvious differentiation in parameter space. By optimizing the skip connections in MT-ResNet, we effectively resolve the issue of details information loss or incompleteness caused by the transmission of impaired optical signals in the neural network. The simulation results demonstrate that the identification success rate can reach 100 % for two common BRs, five mainstream MFs, and seven residual CD values with different impairment degrees. The mean absolute errors (MAEs) of OSNR and residual DGD estimates are 0.42 dB and 0.014 times symbol period respectively. The scheme has excellent tolerance for fiber nonlinear effects. In experimental verification, the accuracies of BRI and MFI are 100 %, and the MAEs of corresponding OSNR estimation for PDM-QPSK/16QAM/32QAM are 0.25 dB, 0.36 dB, and 0.40 dB, respectively. Compared with the existing typical schemes, our scheme significantly improves performance and reduces complexity while simultaneously monitoring a large number of parameters.

Fast adaptation of multi-task meta-learning for optical performance monitoring.

An algorithm is proposed for few-shot-learning (FSL) jointing modulation format identification (MFI) and optical signal-to-noise ratio (OSNR) estimation. The constellation diagrams of six widely-used modulation formats over a wide range of OSNR (10-40 dB) are obtained by a dual-polarization (DP) coherent detection system at 32 GBaud. We introduce auxiliary task to model-agnostic meta-learning (MAML) which makes the gradient of meta tasks decline faster in the direction of optimal target. Ablation experiments including multi-task model-agnostic meta-learning (MT-MAML), single-task model-agnostic meta-learning (ST-MAML) and adaptive multi-task learning (AMTL) are executed to train a data set with only 20 examples for each class. First, we discuss the impact from the number of shots and gradient descent steps for support set on the meta-learning based schemes to determine the best hyper parameters and conclude that the proposed method better captures the similarity between new and previous knowledge at 4 shot and 1 step. Withdrawn fine-tuning, the model achieves the lowest error ∼0.37 dB initially. Then, we simulate two other schemes (AMTL and ST-MAML), and the numerical results shows that mean square error (MSE) are ∼0.6 dB, ∼0.3 dB and ∼0.18 dB, respectively, proposed method has faster adaption to main task. For low order modulation formats, the proposed method almost reduces the error to 0. Meanwhile, we reveal the degree of deviation between the prediction and target and find that the deviation is mainly concentrated in the high OSNR range of 25-40 dB. Specifically, we investigate the variation curve of adaptive weights during pretraining and conclude that after 30 epoch, the model's attention was almost entirely focused on estimating OSNR. In addition, we study the generalization ability of the model by varying the transmission distance. Importantly, excellent generalization is also experimentally verified. In this paper, the method proposed will greatly reduce the cost for repetitively collecting data and the training resources required for fine-tuning models when OPM devices need to be deployed at massive nodes in dynamic optical networks.

Survey on Applications of Machine Learning in Low-Cost Non-Coherent Optical Systems: Potentials, Challenges, and Perspective

Direct Detection (DD) optical performance monitoring (OPM), Modulation Format Identification (MFI), and Baud Rate Identification (BRI) are envisioned as crucial components of future-generation optical networks. They bring to optical nodes and receivers a form of adaptability and intelligent control that are not available in legacy networks. Both are critical to managing the increasing data demands and data diversity in modern and future communication networks (e.g., 5G and 6G), for which optical networks are the backbone. Machine learning (ML) has been playing a growing role in enabling the sought-after adaptability and intelligent control, and thus, many OPM, MFI, and BRI solutions are being developed with ML algorithms at their core. This paper presents a comprehensive survey of the available ML-based solutions for OPM, MFI, and BFI in non-coherent optical networks. The survey is conducted from a machine learning perspective with an eye on the following aspects: (i) what machine learning paradigms have been followed; (ii) what learning algorithms are used to develop DD solutions; and (iii) what types of DD monitoring tasks have been commonly defined and addressed. The paper surveys the most widely used features and ML-based solutions that have been considered in DD optical communication systems. This results in a few observations, insights, and lessons. It highlights some issues regarding the ML development procedure, the dataset construction and training process, and the solution benchmarking dataset. Based on those observations, the paper shares a few insights and lessons that could help guide future research.

Combined image Hough transform based simultaneous multi-parameter optical performance monitoring for intelligent optical networks

The fast-emerging flexible optical networks require intelligent monitoring techniques for supervising the signal quality performance to achieve the desired efficiency. In this paper, we propose a multi-task convolutional neural network (MT-CNN) for multi-parameter monitoring using a unique combined image Hough Transform (CIHT) technique. The proposed system supports simultaneous modulation format identification (MFI), transmission distance detection (TDD), roll-off factor identification (ROFI), Laser linewidth detection (LLWD), centre wavelength detection (CWLD) along with the optical signal-to-noise ratio (OSNR) estimation. The proposed scheme realizes the multi-parameter optical performance monitoring using the advantage of Hough transform (HT) for efficient feature extraction. Rigorous simulations to prove the advantage of using Hough transform, and data augmentation has been performed to conclude the results achieved. The simulation results demonstrate 100% accuracy after 112 epochs for MFI, TDD, ROFI, LLWD, and CWLD, and the mean absolute error (MAE) for OSNR estimation has reduced to 0.354 dB after 180 epochs. Also, rigorous simulations with various conditions are performed to demonstrate the merits of the proposed method.

Low-Complexity Modulation Format Identification Based on Amplitude Histogram Distributions for Digital Coherent Receivers

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 and OSNR Monitoring Based on Multi-Feature Fusion Network

In this paper, we propose a multi-feature fusion network (MFF-Net) for a modulation format identification (MFI) and optical signal-to-noise ratio (OSNR) monitoring scheme. The constellation map data used in this work comes from five modulation formats, namely 56 Gbit/s 4/8 phase shift keying (PSK) and 16/32/64 quadrature amplitude modulation (QAM). The constellation maps are input to one branch network of the MFF-Net, and then the constellation maps are processed by horizontal projection and used as input to another branch network as a way to fuse the two image features. The results show that the scheme achieves 100% MFI accuracy and 98.82% OSNR monitoring accuracy for the five modulation formats. In addition, the performance of MFF-Net and binarized convolutional neural network (B-CNN), visual geometry group network (VGG-Net), and traditional weighted multi-task learning (EW-MTL) are compared to present the superiority of the method. The effect of model structure on MFF-Net is also discussed. The robustness of the model is also evaluated for different transmission distances and bit rates.

Dual Optical Frequency Comb Neuron: Co‐Developing Hardware and Algorithm

Previous studies on photonic neural network have demonstrated that algorithm can inspire hardware design. This study seeks to demonstrate that hardware can also inspire algorithm design. To further exploit the advantages of photonic analog computing, the authors develop hardware and algorithm simultaneously for photonic convolutional neural networks. Specifically, this work developed an architecture called dual optical frequency comb neuron (DOFCN) enabled by an integrated microcomb to perform cosinusoidal nonlinear activation and vector convolution without temporal or spatial dispersion and large‐scale modulator arrays. Furthermore, DOFCN‐based composite vector convolutional neural networks (CVCNNs), an optical‐electric hybrid model, are proposed to perform classification and regression tests in signal modulation format identification and optical structure inverse design tasks, respectively. The ablation experiments show that under 4‐bit precision limit, the element‐wise activation CVCNN has 14% higher classification accuracy, 76% lower regression residuals, and 100% higher training efficiency than that of the 32‐bit standard convolutional neural network (CNN). DOFCN exhibits impressive spectral information processing ability to facilitate signal‐processing tasks related to optics and electromagnetics.

Modulation Format Identification Technology Based on a Searching Cluster Boundary Clustering Algorithm

In this study, a modulation format identification (MFI) scheme based on a searching cluster boundary (SCB) algorithm is designed for space optical communication. Particularly, we design a clustering algorithm based on local low-density samples as the boundaries of clusters and use the boundary to distinguish different clusters; this is unaffected by the distribution of samples in the clusters. The SCB algorithm was applied to MFI, and a verification experiment for space optical communication was performed. The experimental results show that the SCB algorithm achieves good results with clusters of different shapes and the designed MFI scheme can accurately identify modulation formats of different orders with few symbols.

Cascaded modulation format identification and optical signal-to-noise ratio estimation method for optical communication system

Modulation format identification (MFI) and optical performance monitoring are important for elastic optical networks. We propose a cascaded module-based MFI and optical signal-to-noise ratio (OSNR) estimation method. Polar coordinates were used as features to distinguish the modulation formats (MFs) and estimate the OSNR. Dilated convolution was adopted to increase the receptive field and reduce computational complexity. Four commonly used MFs were investigated: dual-polarization (DP) quadrature phase shift keying, 8QAM, 16QAM, and 32QAM. A 32G baud DP transmission system is established, and the results are discussed. The results show that MFI accuracy can reach 100% when the OSNR is less than the 7% forward error correction limit, and the accuracy of OSNR monitoring is close to 100%. Two factors, sample length and nonlinearity, were further investigated. The results show that the MFI and OSNR estimators can achieve perfect performance when the sample length is 3000. Simultaneously, the accuracy of the MFI remained at 100%, and the accuracy of the OSNR estimator decreased by 1% to 2% when the launch power changed from 0 to 5 dBm. Furthermore, two modules with the same structure, DC-CNN and CNN, were compared. The results show that the two models can achieve similar accuracies and that the DC-CNN has the least number of parameters. Finally, experimental verification was conducted to ensure the practical applicability of the proposed method.

Self-evaluation clustering scheme for modulation format identification in coherent optical communication

A self-evaluation clustering scheme for modulation format identification in coherent optical communication is proposed. The performance of the scheme is tested by numerical simulations on a 28 Gbaud polarization division multiplexing (PDM) system. The results show that the proposed scheme achieves an identification accuracy of 100 % and has a significant tolerance to residual chromatic dispersion for PDM-4QAM, PDM-16QAM, PDM-32QAM, and PDM-64QAM signals when the values of optical signal-to-noise ratio are lower than the corresponding 7 % forward error correction thresholds. Moreover, the 20 Gbaud fiber transmission experiments of PDM-4QAM / 16QAM / 32QAM signals along the 2000 km, 1000 km, and 400 km standard single-mode fiber channel were also performed, respectively. The results show that the proposed scheme is robust towards long-haul fiber transmission.

Simultaneous modulation format identification and OSNR monitoring based on optoelectronic reservoir computing.

An approach for simultaneous modulation format identification (MFI) and optical signal-to-noise ratio (OSNR) monitoring in digital coherent optical communications is proposed based on optoelectronic reservoir computing (RC) and the signal's amplitude histograms (AHs) obtained after the adaptive post-equalization. The optoelectronic RC is implemented using a Mach-Zehnder modulator and optoelectronic delay feedback loop. We investigate the performance of the proposed model with the number of symbols, bins of AHs and the hyperparameters of optoelectronic RC. The results show that 100% MFI accuracy can be achieved simultaneously with accurate OSNR estimation for different modulation formats under study. The lowest achievable OSNR estimation mean absolute errors for the dual-polarization (DP)-quadrature phase-shift keying signal, the DP-16-ary quadrature amplitude modulation (16QAM) signal, and the DP-64QAM signal are 0.2 dB, 0.32 dB and 0.53 dB, respectively. The robustness of the proposed scheme is also evaluated when the optoelectronic RC is in presence of additive white Gaussian noises. Then, a proof of concept experiment is demonstrated to further verify our proposed method. The proposed approach offers a potential solution for next-generation intelligent optical performance monitoring in the physical layer.

Joint nonlinear optical signal-to-noise ratio estimation and modulation format identification based on constellation-points trajectory information and multitask 1DCNN for WDM systems.

We propose a joint monitoring scheme of nonlinear optical signal-to-noise ratio (O S N R N L ) estimation and modulation format identification (MFI) in wavelength division multiplexing (WDM) systems. Based on the abundant information of both nonlinear noise (NLN) and modulation format (MF) in received signals, this scheme first counts the trajectory information of all adjacent constellation points, and then quantifies them into the adjacent matrix. Subsequently, the eigenvectors corresponding to the largest eigenvalues are extracted via eigen-decomposition of the adjacent matrix, which characterize the information of NLN and MF effectively. Finally, the eigenvectors are fed into multitask one-dimensional convolutional neural network to perform O S N R N L estimation and MFI simultaneously. To verify the effectiveness of the scheme, five-channel 28 GBaud polarization division multiplexing (PDM) -16/32/64 quadrature amplitude modulation (QAM) WDM simulation systems are built by VPI. The simulation results demonstrate that, for PDM-16/32/64QAM signals, the mean absolute errors of O S N R N L estimation are 0.18, 0.17, and 0.20dB, respectively. At the same time, the identification accuracy rates of these three MFs have achieved 100% within the ranges of estimated O S N R N L . Furthermore, a three-channel 28 GBaud WDM experimental system is constructed to further investigate the effectiveness of trajectory information for O S N R N L estimation. The experimental results show that the O S N R N L estimation errors of PDM-16QAM are less than 0.5dB. In addition, our analysis of complexity from two aspects of trajectory information extraction and neural network model shows that the overall complexity scale of this scheme is O(K i,3 M C i,3 C o,3).