Modulation Format Identification Research Articles

Virtualized PON based on abstraction, softwarization, and service chaining for flexible and agile service creations [Invited

The specifications of passive optical network (PON) systems continue to evolve while access systems need to accommodate an ever-widening variety of new services. To deal with the various requests, virtualization is essential for optical access networks. So far, optical line terminal (OLT) abstraction technology, represented by software-defined-network-enabled broadband access, has been commercialized as a platform, but a remaining challenge is the softwarization of media access control and physical-layer (PHY) processing as OLT functionalities. However, in the virtualized PON, service chaining (SC) is also crucial to realize efficient access networks that meet user requirements by chaining the virtualized functions. In particular, no study, to our knowledge, has focused on PHY SC classification that supports adaptive coding and modulation. This paper reviews recent advances in PON virtualization techniques including abstraction and softwarization while showing our target optical access system, which is fully virtualized. Furthermore, it proposes an SC classifier adopting low-complexity modulation format identification (MFI). Experiments show that our proposed method achieves 100% identification accuracy if the bit error rate is within the forward error correction limit; real-time processing speed of 2.5 Gsymbols/s with 3% computation resource occupancy is achieved, as the proposal has much lower computation overheads than other MFI methods.

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

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.

Photonic-Assisted Modulation Format Identification for RF Signals under Low Sampling Rate

A photonic-assisted approach is proposed for modulation format identification (MFI) on radio frequency (RF) signals under low sampling rate. In this approach, a photonic-assisted interferometer (PAI) is designed for computation-free data augmentation by transforming the signal’s phase and frequency variations into modulation format-sensitive amplitude features. A fully connected neural network (FCNN) is used to implement end-to-end MFI. An experiment is conducted on the identification of amplitude-shift keying (ASK), binary phase-shift keying (BPSK), quadrature phase-shift keying (QPSK), frequency-shift keying (FSK), and linear frequency modulation (LFM) signals with signal-to-noise ratios (SNRs) from −10 to 15 dB and carrier frequencies within 5 to 10 GHz. The results show that the proposed photonic-assisted modulation format identifier (PA-MFI) achieves the identification accuracy of 82.44% at 1 GHz sampling rate, which is 10.6% higher than the accuracy of direct modulation format identification (Direct-MFI) without PAI processing.

Linear Regression vs. Deep Learning for Signal Quality Monitoring in Coherent Optical Systems

Error vector magnitude (EVM) is a metric for assessing the quality of m-ary quadrature amplitude modulation (mQAM) signals. Recently proposed deep learning techniques, e.g., feedforward neural networks (FFNNs) -based EVM estimation scheme leverage fast signal quality monitoring in coherent optical communication systems. Such a scheme estimates EVM from amplitude histograms (AHs) of short signal sequences captured before carrier phase recovery (CPR). In this work, we explore further complexity reduction by proposing a simple linear regression (LR) -based EVM monitoring method. We systematically compare the performance of the proposed method with the FFNN-based scheme and demonstrate its capability to infer EVM from an AH when the modulation format information is known in advance. We perform both simulation and experiment to show that the LR-based EVM estimation method achieves a comparable accuracy as the FFNN-based scheme. The technique can be embedded with modulation format identification modules to provide comprehensive signal information. Therefore, this work paves the way to design a fast-learning scheme with parsimony as a future intelligent OPM enabler.

Blind and low-complexity modulation format identification based on signal envelope flatness for autonomous digital coherent receivers.

Modulation format identification (MFI) is a critical technology for autonomous digital coherent receivers in next-generation elastic optical networks. A novel and simple MFI scheme, to the best of our knowledge, based on signal envelope flatness is proposed without requiring any training or other prior information. After amplitude normalization and partition, the incoming polarization division multiplexed (PDM) signals can be classified into quadrature phase shift keying (QPSK), 8 quadrature amplitude modulation (QAM), 16QAM, and 64QAM signals according to envelope flatnesses R1, R2, and R3 of signals in different amplitude ranges. The feasibility of the proposed MFI scheme is first verified via numerical simulations with 28 GBaud PDM-QPSK/-8QAM/-16QAM/-64QAM signals. Only by using 4000 symbols can the proposed MFI scheme achieve a 100% correct identification rate for the four modulation formats over a wide optical signal-to-noise ratio (OSNR) range. Proof-of-concept experiments among 28 GBaud PDM-QPSK/-8QAM/-16QAM systems under back-to-back and long-haul fiber transmission links are implemented to further demonstrate the effectiveness of the proposed MFI scheme. The experimental results show that the proposed MFI scheme can obtain a 100% correct identification rate when the OSNR value of each modulation format is higher than the threshold corresponding to 7% FEC and is resilient towards fiber nonlinearities. More importantly, the proposed MFI scheme can significantly reduce computational complexity.

Intelligent Optical Performance Monitoring Based on Intensity and Differential-Phase Features for Digital Coherent Receivers

An intelligent optical performance monitoring scheme for simultaneous modulation format identification (MFI) and optical signal-to-noise ratio (OSNR) monitoring is proposed and experimentally demonstrated in digital coherent receivers. The proposed scheme introduces convolutional neural network (CNN) to automatically extract and monitor modulation format and OSNR dependent features (the empirical distribution of intensity and differential-phase) which can be obtained after constant modulus algorithm (CMA) equalization. The experiment results show that 100% identification accuracies for all modulation formats (e.g. 28-GBaud PDM-QPSK/−8PSK/ −8QAM/−16QAM/−32QAM/−64QAM) are achieved at OSNR values are lower than the corresponding theoretical 20% FEC limit (BER = 2.4×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−2</sup> ). Furthermore, under the chromatic dispersion (CD) variation from 0-ps/nm to 1200-ps/nm, the root-mean- square error (RMSE) and mean absolute error (MAE) of OSNR monitoring for all modulation formats are less than 0.0896-dB and 0.0657-dB, respectively. Subsequently, the influence of frequency offset and fiber nonlinearity effect on the intelligent optical performance monitoring scheme is also analyzed. We believe that our proposed multi-parameter monitoring scheme has the potential to be applied in the next-generation intelligent elastic optical networks.

Intelligent equally weighted multi-task learning for joint OSNR monitoring and modulation format identification

A novel scheme for combining modulation format identification (MFI) and optical performance monitoring (OPM) in elastic optical networks using convolutional-neural-network-based and equally weighted multi-task learning is proposed. In this scheme, shared features of optical signal-to-noise ratio (OSNR) and modulation formats (10 GBaud QPSK, QAM8, QAM16, QAM64) are extracted from constellation maps and used for multi-label classification. Simulation results demonstrate that the accuracies of MFI and OSNR can reach 100% stably and continuously. Moreover, we evaluated the robustness of this model in a system with fixed physical parameters and concluded that it is tolerant to chromatic dispersion and transmission distance. This technology predicts an image in ∼0.51 ms. Compared with the general multi-task learning (MTL) scheme, the proposed approach massively saves execution time (∼107 h) by removing weight selection. It is time-efficient and intelligent for OPM equipment that will perform multiple monitoring tasks in next-generation optical networks.

Modulation Format Identification Using Supervised Learning and High-Dimensional Features

Modulation Format Identification Using Supervised Learning and High-Dimensional Features

Transfer learning approach toward joint monitoring of bit rate and modulation format.

Convolutional neural network based transfer learning (TL) is proposed to achieve joint optical performance monitoring with bit rate and modulation format identification in optical communication systems. TL is used to improve the execution of various tasks by extracting features without knowing other optical link parameters. Eye diagrams of four different modulation formats are generated at optical signal-to-noise ratios (OSNRs) varying from 15 to 30 dB for two distinct bit rates, which are then identified simultaneously with a trained deep neural network. In addition, comparisons of different TL approaches are presented. The database is divided into distinct categories with varying parameter ranges in offline mode, and prediction models are assigned to each class. The results suggest that the proposed system may greatly increase identification performance over existing strategies by utilizing TL techniques. The impacts of training, testing, and validation data size, as well as model structure based on TL, are also thoroughly investigated. The results reveal that the VGG16 achieves the highest accuracies compared to other deep learning algorithms even at low OSNR values of 20 dB. The proposed structure can intelligently evaluate the signals of future heterogeneous optical communications, and the results can be used to enhance optical network management.

Optical performance monitoring in transparent fiber-optic networks using neural networks and asynchronous amplitude histograms

Recently, technologies such as artificial neural networks (ANNs) and asynchronous amplitude histograms (AAHs) are widely employed in the optical performance monitoring (OPM). In this paper, the number of layers and the optimization of the neural networks are investigated to complete a series of monitoring tasks in optical communication systems with a large range of chromatic dispersion (CD) and optical signal-to-noise ratio (OSNR). It is shown that the monitoring accuracy has been significantly improved with such implementations. Numerical simulations, which have been conducted for six different modulation formats, demonstrate a good monitoring performance, with an average OSNR monitoring error of 0.1064 dB (99.49% monitoring accuracy) for the OSNR range of 10–40 dB and an average CD monitoring error of 3.3324 ps/nm (99.45% monitoring accuracy) for the CD range of 170–1870 ps/nm, respectively. Furthermore, the CD monitoring and the modulation format identification (MFI) have also been investigated for the system with a large range of dispersion. An average CD error of 16.6832 ps/nm (99.45% monitoring accuracy) and a 100% MFI accuracy have been achieved for all six modulation formats. This scheme is also verified experimentally with OSNR errors of 0.41 dB and CD errors of 15.21 ps/nm, respectively. The identification accuracy of the three modulation formats in the experimental system is also 100%.

Performance Investigation of Modulation Format Identification in Super-Channel Optical Networks

The problem of automatic modulation format identification (MFI) is one of the main challenges in adaptive optical systems. In this work, we investigate MFI in super-channel optical networks. The investigation is conducted by considering the classification of seven multiplexed channels of 20 Gbaud, each with six commonly used modulation formats, including polarization division multiplexing (PDM)-BPSK, PDM-QPSK, and PDM-MQAM with (M = 8, 16, 32, 64). The classification performance is assessed under different values of optical signal-to-noise ratio (OSNR) and in the presence of channel interference, channel chromatic dispersion, phase noise, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$1^{st}$</tex-math></inline-formula> polarization mode dispersion (PMD). Furthermore, the effect of fiber nonlinearity on the MFI accuracy is investigated. A well-established machine learning algorithm based on histogram features and a convolutional neural network has been used in this investigation. Results indicate that accurate identification accuracy can be achieved within the OSNR range of practical systems and that the MFI accuracy of side subcarriers outperforms that of middle subcarriers at a fixed value of OSNR. The results also show that the MFI accuracy of PDM-16QAM and PDM-64QAM are affected more by channel interference than the other modulation formats, especially when the ratio of the subcarrier bandwidth to subcarriers spacing is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\geq$</tex-math></inline-formula> 1.4. Finally, laboratory experiments have been conducted for validation purposes. The experimental results were found in good agreement with those achieved by simulation.

Optical signal monitoring using multi-task residual network

Optical signal monitoring, including optical performance monitoring (OPM) and bit rate and modulation format identification (BR-MFI), has attracted considerable interest owing to the advantages in automatic system diagnosis and the establishment of adaptive optical networks. However, the lack of effective multi-parameter shared feature extraction methods makes the optical signal monitoring still elusive and hinders its practical application. Exploiting shortcut connections that allow features to propagate across multiple layers to effectively reduce the low-level feature loss, we introduce a multi-task residual network (MT-ResNet) to extract optical signal feature information from eye diagrams. The results show that the model exhibits a multi-parameter shared feature extraction ability and OPM and BR-MFI can be directly realized. The average root-mean-square errors of optical signal-to-noise ratio, chromatic dispersion, and differential group delay monitoring are 0.84 dB, 1.8 ps/nm, and 0.18 ps, respectively. The accuracies of BR-MFI reach 100%. We demonstrate that the model is robust by investigating its feature extraction ability in the case of increased fiber nonlinearity and untrained samples, and the universality of the model is proved by applying it to a long-reach optical coherent communication system. Our simulation results indicate that this MT-ResNet can be deployed to analyze the signal quality cost-effectively, with a high potential for future dynamic and heterogeneous optical networks.

Modulation Format Identification in a Satellite to Ground Optical Wireless Communication Systems Using a Convolution Neural Network

The satellite-to-ground communication system is a significant part of future space communication networks. The free-space optical (FSO) communication technique is a prospective solution for satellite-to-ground communication. However, atmospheric optical turbulence is a major impairment in FSO communication systems. In this paper, to improve the performance and flexibility of a satellite-to-ground laser communication system, we put forward a novel modulation format identification (MFI) technique for an FSO communication system based on a convolution neural network (CNN). The results indicate that our CNN model can blindly and accurately identify the modulation format with classification accuracy up to 99.98% for random channel condition, including the strength of turbulence and signal-to-noise ratio (SNR) of additive Gaussian white noise (AWGN) ranging from 10dB to 30dB. Moreover, the CNN demonstrated robustness against atmospheric optical turbulence and suggested immunity to additive noise. Therefore, the proposed methodology proved to be a viable solution in the application of an FSO communication simulation channel, which can easily deal with the scene of fast modulation format switching and accurate identification to satisfy system requirements. Therefore, we hope this scheme can find a practical implementation in satellite-to-ground optical wireless systems.

Simultaneous baud rate/modulation format identification and multi-parameter optical performance monitoring using multi-task learning with enhanced picture of Radon transform

We propose a simultaneous baud rate identification (BRI), modulation format identification (MFI) and multi-parameter optical performance monitoring (OPM) scheme for elastic optical networks (EONs). Firstly, based on the advantage that enhanced picture of Radon transform (EPRT) contains more phase, amplitude, and density information of signals, the proposed scheme takes EPRT, which is obtained from the original constellation diagram of EON signals after pretreatment of grayscale processing, edge detection and Radon transform, as the key feature for signal parameter identification and monitoring. Subsequently, the EPRTs are fed into a multi-task learning (MTL) neural network to perform BRI, MFI, chromatic dispersion identification (CDI), differential group delay (DGD) monitoring and optical signal-to-noise ratio (OSNR) estimation simultaneously. The numerical results of 14/28 GBaud polarization division multiplexing (PDM)-EON transmission system demonstrate that the accuracies of BRI, MFI and CDI could achieve almost 100% after 105 epochs, 114 epochs and 174 epochs respectively. For the task of OSNR estimation, its mean absolute errors (MAE) converges to 0.468 dB after 187 epochs, and the MAE of DGD monitoring is stable at 0.021 times symbol period after 170 epochs. Furthermore, the effectiveness of the proposed scheme is further verified by 14/28 GBaud PDM-EON proof-of-concept experimental system. The results manifest that the MFI and BRI accuracy achieve almost 100% after 118 epochs and 175 epochs, respectively. For the OSNR estimation task, its MAE converges to 0.542 dB after 200 epochs.

Joint multi-parameter optical performance monitoring scheme based on trajectory information for a Stokes vector direct detection system.

In this study, we propose and verify a joint multi-parameter optical performance monitoring (OPM) scheme based on trajectory information for the Stokes vector direct detection (SVDD) system, for the first time, to the best of our knowledge. Here, the proposed scheme first performs quantification of the trajectory to construct trajectory information, which not only presents diversity of the received symbols in spatial dimension, but also records the jump pattern among symbols in time dimension. Subsequently, eigenanalysis is introduced to extract critical features hidden in trajectory information and simultaneously achieve the purpose of dimensionality reduction. The effectiveness of the scheme is verified through 14/28 GBaud SVDD binary phase shift keying/quadrature phase shift keying/-8 quadrature amplitude modulation (QAM)/-16QAM/-32QAM/-64QAM simulation systems. Under the scenario of joint modulation format (MF) identification and optical signal to noise ratio (OSNR) monitoring, the identification rates of all six kinds of MFs achieve 100% within their corresponding reasonable OSNR ranges. Besides that, the average mean absolute error (MAE) of the monitored OSNRs are obtained as 0.03 dB, 0.22 dB, 0.36 dB, 0.41 dB, 0.46 dB, and 0.49 dB for those six kinds of MFs, respectively. Under the scenario of multi-parameter OPM, SVDD-8QAM/-16QAM/-32QAM signals are 100% successfully identified when residual chromatic dispersion (RCD) is located in the ranges of 0-200 ps/nm, 0-190 ps/nm, and 0-160 ps/nm, respectively. The average MAE of OSNR monitoring and RCD estimation for these three commonly used MFs are 1.08 dB and 3.23 ps/nm, respectively. Moreover, the study also demonstrates the robustness for baud rates and a relatively simpler calculation complexity about the proposed OPM scheme.

Blind Modulation Format Identification Based on Principal Component Analysis and Singular Value Decomposition

As optical networks evolve towards flexibility and heterogeneity, various modulation formats are used to match different bandwidth requirements and channel conditions. For correct reception and efficient compensation, modulation format identification (MFI) becomes a critical issue. Thus, a novel blind MFI method based on principal component analysis (PCA) and singular value decomposition (SVD) is proposed. Based on square operation and PCA, the influence of phase rotation is removed, which avoids phase rotation-related discussions and training. By performing SVD on the density matrix about constellation, a denoise method is implemented and the quality of the constellation is improved. In the subsequent processing, the denoised density matrix is used as the feature of the support vector machine (SVM), and the identification of seven modulation formats such as BPSK, QPSK, 8PSK, 8QAM, 16QAM, 32QAM and 64QAM is realized. The results show that lower OSNR values are required for the 100% accurate identification of all modulation formats to be achieved, which are 5 dB, 7 dB, 8 dB, 11 dB, 14 dB, 14 dB and 15 dB. Moreover, the proposed method still retains the advantage, even when the number of samples decrease, which is beneficial for low-complexity implementation.

Multi-Carrier Signal Recognition Method Based on Multi-Feature Input and Hybrid Training Neural Network

In order to achieve automatic identification of modulation formats in orthogonal frequency division multiplexing (OFDM) subcarrier signals, a recognition method based on multiple feature inputs and a Hybrid Training Neural Network (HTNN) is proposed, in which an HTNN structure is designed to obtain high-order statistical correlation features and constellations of OFDM subcarriers. The recognition performance of the proposed method in free space channel transmission and atmospheric time-varying channel transmission is studied by simulation. Simulation results show that the overall identification accuracy of the recognition model based on the proposed method exceeded 93.37% in the wide Signal-to-Noise Ratio (SNR) range of the free space channel. With an SNR higher than 7.5 dB, identification accuracy performance of the learning model culminated, achieving 100% identification accuracy of OFDM subcarrier signals. Under weak turbulent atmospheric and time-varying channel conditions, the overall identification accuracy curve tended to increase as SNR increased and stabilized at more than 95%. Compared with the two comparison methods, the proposed method reduced the sensitivity to channel variations and improved the convergence speed on the basis of the guaranteed identification accuracy, and enabled reliable identification of OFDM subcarrier signals in a wide SNR range.

Fan-beam projection for modulation classification in optical wireless communication systems.

The focus of most research nowadays in the field of communication technology is on increasing bandwidth in wireless connectivity. Adaptive modulation can be used to enhance efficiency of communication systems. Adaptive modulation requires modulation format identification (MFI) at the receiver side to avoid the overhead required to determine the modulation type at the receiver. We present an MFI algorithm based on fan-beam projection to generate patterns from the constellation diagrams that are more discriminative. The constellation diagrams are obtained as images for eight different modulation formats (2/4/8/16 - PSK and 8/16/32/64 - QAM). Different classifiers such as AlexNet, VGG16, and VGG19 are studied and compared for the task of MFI. Evaluation of this proposed algorithm is performed by estimating the classification accuracy at different optical signal-to-noise ratios (OSNRs) ranging from 5 to 30 dB. The simulation results reveal that the proposed algorithm succeeds in identifying the wireless optical modulation format blindly with a classification accuracy up to 100% even at low OSNR values less than 8 dB compared with the related work.

Modulation format identification using the Calinski-Harabasz index.

Modulation format identification (MFI) is a key technology in optical performance monitoring for the next-generation optical network, such as the intelligent cognitive optical network. An MFI scheme based on the Calinski-Harabasz index for a polarization-division multiplexing (PDM) optical fiber communication system is proposed. The numerical simulations were carried out on a 28 Gbaud PDM communication system. The results show that the required minimum optical signal-to-noise ratio values of each modulation format to achieve 100% identification accuracy are all equal to or lower than their corresponding 7% forward error correction thresholds, and the proposed scheme is robust to residual chromatic dispersion. Meanwhile, the proposed scheme was further verified by 20 Gbaud PDM-QPSK/16QAM/32QAM long-haul fiber transmission experiments. The results show that the scheme has a good reliability when fiber non-linear impairments exist. In addition, the complexity of the scheme is significantly lower than that of other clustering-based MFI schemes.

Photonic neuromorphic technologies in optical communications

Abstract Machine learning (ML) and neuromorphic computing have been enforcing problem-solving in many applications. Such approaches found fertile ground in optical communications, a technological field that is very demanding in terms of computational speed and complexity. The latest breakthroughs are strongly supported by advanced signal processing, implemented in the digital domain. Algorithms of different levels of complexity aim at improving data recovery, expanding the reach of transmission, validating the integrity of the optical network operation, and monitoring data transfer faults. Lately, the concept of reservoir computing (RC) inspired hardware implementations in photonics that may offer revolutionary solutions in this field. In a brief introduction, I discuss some of the established digital signal processing (DSP) techniques and some new approaches based on ML and neural network (NN) architectures. In the main part, I review the latest neuromorphic computing proposals that specifically apply to photonic hardware and give new perspectives on addressing signal processing in optical communications. I discuss the fundamental topologies in photonic feed-forward and recurrent network implementations. Finally, I review the photonic topologies that were initially tested for channel equalization benchmark tasks, and then in fiber transmission systems, for optical header recognition, data recovery, and modulation format identification.