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In recent years, many new modulation and multiplexing technologies and dynamic network concepts have been proposed to adapt to the ever-increasing network bandwidth and quality requirements. Network control platform has a systematic and intelli-gent development trend, which requires network managers to constantly monitor the parameters of the network and optimize the network state. However, it is not feasible to arrange additional monitoring equipment in a large range to obtain parameter informa-tion from the perspective of cost control. It is better to use known data and special algorithms to monitor and optimize network per-formance. Machine learning methods are increasingly adopted by the academic community because they are accurate and efficient enough to accomplish these tasks. This paper first reviews the different application scenarios of machine learning algorithms in op-tical network monitoring and optimization tasks. Then it reviews the research achievements in this field, and puts forward the ex-isting problems of machine learning-based optical network monitoring and optimization methods as well as the possible direction of future research. The optical performance monitoring based on machine learning includes failure identification, quality of transmis-sion estimation and channel power prediction. The network configuration optimization method based on machine learning includes reinforcement learning to optimize channel power. For future research direction, we believe that it is possible for researchers to use real data from network operators, newly collected data to dynamically train the model, and transfer learning and data enhancement techniques to ensure the robustness and generalization ability of the algorithm.

Recently, extensive research has been conducted to explore the utilization of machine learning (ML) algorithms in various direct-detected and (self)-coherent short-reach communication applications. These applications encompass a wide range of tasks, including bandwidth request prediction, signal quality monitoring, fault detection, traffic prediction, and digital signal processing (DSP)-based equalization. As a versatile approach, ML demonstrates the ability to address stochastic phenomena in optical systems networks where deterministic methods may fall short. However, when it comes to DSP equalization algorithms such as feed-forward/decision-feedback equalizers (FFEs/DFEs) and Volterra-based nonlinear equalizers, their performance improvements are often marginal, and their complexity is prohibitively high, especially in cost-sensitive short-reach communications scenarios such as passive optical networks (PONs). Time-series ML models offer distinct advantages over frequency-domain models in specific contexts. They excel in capturing temporal dependencies, handling irregular or nonlinear patterns effectively, and accommodating variable time intervals. Within this survey, we outline the application of ML techniques in short-reach communications, specifically emphasizing their utilization in high-bandwidth demanding PONs. We introduce a novel taxonomy for time-series methods employed in ML signal processing, providing a structured classification framework. Our taxonomy categorizes current time-series methods into four distinct groups: traditional methods, Fourier convolution-based methods, transformer-based models, and time-series convolutional networks. Finally, we highlight prospective research directions within this rapidly evolving field and outline specific solutions to mitigate the complexity associated with hardware implementations. We aim to pave the way for more practical and efficient deployment of ML approaches in short-reach optical communication systems by addressing complexity concerns.

In advanced high-speed fiber optic communication systems, due to the introduction of dense wavelength division multiplexing technology, the signal spectral interval is getting narrower and narrower, and the traditional out-of-band Optical Signal-to-Noise Ratio (OSNR) monitoring technology is no longer accurate. Therefore, further study is required in the low-cost in-band OSNR monitoring scheme. A Deep Neural Network (DNN) link OSNR monitoring scheme for Intensity-Modulation and Direct Detection (IMDD) system is proposed. We used a 5-layer DNN trained from 550 000 datasets to successfully estimate the OSNR of the 2 GBaud On-Off Key (OOK) signal in the range of 5 to 15 dB, and the Mean Absolute Error (MAE) is less than 0.8 dB.

The exponentially growing demand for high-speed data necessitates more complex and versatile networks. Optimization and reliability assurance of such high-complexity networks is getting increasingly important. In this article, we experimentally validate our a machine learning-based framework that combines quality of transmission (QoT) estimation with soft-failure detection, identification, and localization based on the same latent space of a variational autoencoder running on optical spectra obtained by optical spectrum analyzers at high priority nodes in the network. We further investigate the advantages of a variational autoencoder-based soft-failure detection mechanism over a QoT metric-based approach. We use data acquired from optical transmission experiments involving different modulation formats and channel configurations. The results demonstrate that the proposed framework achieves reliable QoT estimation in real world scenarios. Additionally, it effectively detects soft-failures, identifies specific failure types and accurately localizes the occurrence of failures.

In this paper, a joint OSNR and nonlinear noise power estimation scheme based on multi-task deep neural network (MT-DNN) is proposed with the advantages of dispersion-insensitive, modulation-format-transparent for high-speed, long-haul, multi-channel fiber-optic communication systems. Amplitude histograms (AHs) are generated by processing the spectrums collected with different OSNR, launch power and transmission distance by an offline spectrum preprocessing flow. The MT-DNN can automatically learn the features of the AHs to achieve OSNR and nonlinear noise power estimation, simultaneously. For 4-quadrature amplitude modulation (4QAM), 16QAM and 64QAM signals under different transmission conditions, the average MAE and RMSE are calculated for the OSNR estimate and the nonlinear noise power estimate, which are both less than 1 dB. Moreover, the resistance of OSNR estimation to amplified spontaneous emission (ASE) noise and nonlinearity, and the tolerance of nonlinear noise estimation to launch power and transmission distance are investigated, respectively. The results demonstrate that the joint OSNR and nonlinear noise power estimation scheme is insensitive to dispersion, transparent to modulation format, and has high accuracy and high tolerance. This research provides a research reference value for future optical performance monitoring of coherent optical communication systems.

In the optical communication systems, the optical spectrum (OS) provides useful informations for optical performance monitoring and optical link diagnosis. In this paper, we investigate the neural-network based OS analysis (OSA) techniques able to simultaneously recognize the OS and estimate the optical signal to noise ratio (OSNR), cascaded filtering distortion (CFD) and carrier wavelength drift (CWD). We demonstrate that, compared with the multi-task artificial neural network (MT-ANN) and convolutional neural network (MT-CNN), the proposed multi-task cascaded ANNs (CANN) and cascaded CNNs (CCNN) can greatly improve the OSA performance and accelerate the training process by exploiting specific features and loss functions for different tasks. The CCNN able to deal with the high-resolution OS (HOS) can further improve the OSA performance compared with the CANN. The averaged OS recognition accuracy and OSNR, CFD and CWD estimation errors obtained with the CCNN (CANN) for the 15 kinds of optical signals are 99.32% (97.07%), 0.36 (0.55) dB, 0.24 (0.32) and 0.04 (0.06) GHz, respectively, even when the various OS distortions are present. The proposed CANN and CCNN with a better versatility, higher OSA accuracy and faster convergence speed are promising enabling techniques for the future intelligent OSA 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.

We propose a simultaneous optical signal recognition (OSR) and optical signal-to-noise ratio (OSNR) monitoring method by using multi-task learning convolutional neural network (MTL-CNN) in conjunction with optical spectrums, which enables optical performance monitoring (OPM) in the optical transmission network. In order to achieve a trade-off between monitoring loss and time consumption of the MTL-CNN constructed for seven commonly used signals with a spectrum resolution of 10 pm, the number of feature map channels in four convolutional layers is delicately designed as 32, 32, 64, and 64 with task weights set to 0.4 and 0.2, corresponding to OSNR monitoring and OSR, respectively. Simulation results manifest that this method can realize the recognition of the received optical signals with an overall accuracy of 100% and also enable OSNR monitoring with the mean absolute error (MAE) of 0.262 dB. The proposed method shows strong robustness to various distortions and exhibits good performance in terms of time consumption. The effectiveness is further verified by proof-of-concept experiments in three signals. These illustrate that our method is a promising solution for multi-parameter monitoring with high accuracy and efficiency.

Weakly-coupled mode-division multiplexing (MDM) technique based on linearly-polarized (LP) modes in ultralow-modal-crosstalk few-mode fiber (FMF) has been considered as a promising candidate for capacity enhancement of next-generation optical transmission systems. Similar to polarization mode dispersion in single-mode fibers (SMFs), signals in weakly-coupled FMFs suffer from the impacts of intra-LP-mode dispersion (ILMD) for both degenerate and non-degenerate LP modes.In this paper, we propose an effective method for ILMD monitoring in weakly-coupled MDM systems. The ILMD evaluation method for degenerate LP modes is designed by the analysis of channel transfer matrix in a digital coherent receiver. Then the ILMD monitoring scheme is experimentally validated for all the 6 LP mods in a weakly-coupled FMF. The influences of chromatic dispersion, optical signal to noise ratio and inter-LP-mode crosstalk to the method are also investigated.

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.

We propose a cascaded neural network (NN) to simultaneously identify the modulation formats and monitor the optical-signal-to-noise ratio (OSNR). In the second-level network, it is a single deep NN (DNN) rather than multiple sub-networks, which makes the architecture more compact and can save the resource for real implementation. However, since the data set is constituted from all modulation formats, the universality can be guaranteed but not for the accuracy and the complexity. To accelerate the estimation process and improve the accuracy, we introduce the transfer learning (TL) and reconstruct the data set with a part from all of the modulation formats for universality and another part from a specific modulation format for TL to pursue higher accuracy. In the experiment, we compare the proposed cascaded single neural network (CSNN) with or without TL, cascaded multiple neural networks (CMNN) and adaptive multi-task learning (MTL) for MFI and OSNR monitoring. In the first-level NN, all of the three schemes can achieve the accuracy of MFI as 100%. In the second-level NN, the CSNN with TL (TL-CSNN) can significantly improve the training speed and decline the RMSE of 0.19 dB compared with CSNN without TL. The TL-CSNN also has faster convergence speed and is more stable compared with CMNN and adaptive MTL.

Monitoring the state of the optical network is a key enabler for programmability of network functions, protocols and efficient use of the spectrum. A particular challenge is to provide the SDN-EON controller with a panoramic view of the complete state of the optical spectrum. This paper describes an architecture for compact on-chip spectrometry targeting high resolution across the entire C-band to reliably and accurately measure the spectral profile of WDM signals in fixed and flex-grid architectures. An industry standard software tool is used to validate the performance of the spectrometer. The fabrication of the proposed design is found to be practical.

Compared with traditional wavelength division optical network, elastic optical network (EON) divides the network spectrum into smaller spectrum slots to improve the spectrum utilization, but the high-quality spectrum division also complicates the routing and spectrum allocation (RSA) problem. Various strategies are proposed for reducing the RSA complexity and improving system traffic bearing capacity. However, previous RSA strategies do not consider the changing physical layer impairments that will also impact signal quality and even lead to violation of quality of transmission (QoT), the data cannot be transmitted correctly if the link state is degraded. Therefore, cross-layer optimization is desired, which means that different layer information is taken into account in the RSA strategy. In this paper, we propose a new link state-aware (LSA) RSA strategy to guarantee the QoT requirements under different link states. At first, the link state is evaluated based on chromatic dispersion (CD) and optical signal-to-noise ratio (OSNR), and a LightGBM model is exploited for CD and OSNR estimation. In LSA-RSA strategy, the link state is considered as a metric for qualified routing paths finding, and the link capacity is calculated based on the link state and used in spectrum allocation. Simulation results show that the average CD and OSNR estimation errors of the LightGBM model are 0.28ps/nm and 0.68dB, respectively. Under different link states and traffic loads, the LSA-RSA strategy can reduce traffic failure probability by more than 20%, and traffic load can increase 40Erlang when the bandwidth blocking probability equals 10%.

Future communication systems are faced with increased demand for high capacity, dynamic bandwidth, reliability and heterogeneous traffic. To meet these requirements, networks have become more complex and thus require new design methods and monitoring techniques, as they evolve towards becoming autonomous. Machine learning has come to the forefront in recent years as a promising technology to aid in this evolution. Optical fiber communications can already provide the high capacity required for most applications, however, there is a need for increased scalability and adaptability to changing user demands and link conditions. Accurate performance monitoring is an integral part of this transformation. In this paper, we review optical performance monitoring techniques where machine learning algorithms have been applied. Moreover, since many performance monitoring approaches in the optical domain depend on knowledge of the signal type, we also review work for modulation format recognition and bitrate identification. We additionally briefly introduce a neuromorphic approach as an emerging technique that has only recently been applied to this domain.

في هذا البحث تم اقتراح تقنية جديدة لمراقبة تشتت نمط الاستقطاب polarization mode dispersion (PMD) في شبكات الألياف الضوئية. تعتمد التقنية على تحويل مركبتي الاستقطاب المتعامدتين للإشارة الضوئية المراقبة إلى نفس حالة الاستقطاب ومن ثم يتم جمعهما معاً بفرق طور (180 درجة). يتم قياس مطال النبضات الناتجة والتي تتناسب مع تأخير المجموعة التفاضلية differential group delay (DGD) الذي يصف تشتت (PMD) لوصلة الليف الضوئي. تتميز التقنية المقترحة مقارنة بتقنيات المراقبة الأخرى، بمجال مراقبة جيد، وحساسية عالية، وعدم الحاجة لتعديل جهاز الإرسال، وبنية بسيطة، وعدم تأثرها بالتشتت اللوني (CD، وزمن استجابة سريع والقدرة على العمل بمعدلات بت مختلفة. تم استخدام برنامج المحاكاة الضوئي VPIphotonics لإثبات صحة عمل وحدة المراقبة المقترحة، وتحليل أدائها.

Optical performance monitoring (OPM), particularly the optical power and optical signal-to-noise ratio (OSNR) of each wavelength channel, are of great importance and significance and need to be implemented to ensure stable and efficient operation/maintenance of wavelength division multiplexing (WDM) networks. However, the critical monitoring module of existing solutions generally are too expensive, operationally inconvenient and/or functionally limited to apply over WDM systems with numerous nodes. In this paper, a low-cost and high-efficiency OPM scheme based on differential phase shift keying (DPSK)-modulated digital optical labels is proposed and demonstrated. Each pilot tone is modulated by digital surveillance information and treated as an identity indicator and performance predictor that ties up to each wavelength channel and thereby can monitor the performance of all wavelength channels simultaneously by only one low-bandwidth photoelectric detector (PD) and by designed digital signal processing (DSP) algorithms. Simulation results showed that the maximum errors of channel power monitoring and OSNR estimation were both less than 1 dB after 20-span WDM transmission. In addition, offline experiments were also carried out and further verified the feasibility of our OPM scheme. This confirms that the optical label based OPM has lower cost and higher efficiency and thereby is of great potential for mass deployment in practical WDM systems.

A machine learning (ML)-based multifunctional optical spectrum analysis technique is proposed to perform not only the conventional analysis functions but also the extended analysis functions, including center wavelength detection, optical signal-to-noise (OSNR) calculation, bandwidth recognition, as well as spectral distortion diagnosis. We have investigated four widely used ML algorithms, including support vector machine (SVM), artificial neural network, k-nearest neighbors, and decision tree. First, the wavelengths, OSNRs, and bandwidths of optical signals are processed by four ML methods based on the spectral data. The good performance and fast processing speed are obtained, especially for SVM, achieving the optimal accuracy (100%) and the least test time. For the need of the practical application, we also investigate the more complicated case, where wavelength, OSNR, and bandwidth are variable simultaneously so that the ML should analyze these three parameters comprehensively. Even in this case, the overall accuracy is still larger than 99.1%. In addition, the extended analysis functions are also studied to diagnose the spectral distortion caused by the cascaded filtering effect and off-center filtering effect. The number of cascaded filters and the offsets of filter shift and laser drift can be effectively estimated by the SVM with high average accuracy and low standard deviation, which are useful for failure detection and distortion recovery. This technique has the potential to be applied in the optical spectrum analyzer to implement the multifunctional spectrum analysis or in the optical performance monitor to execute the spectral distortion diagnosis.

In this paper, and for the first time in literature, optical performance monitoring (OPM) of super-channel optical networks is considered. In particular, we propose a novel machine learning OPM technique based on the use of transformed in-phase quadrature histogram (IQH) features and support vector regressor (SVR) to estimate different optical parameters such as optical signal-to-noise ratio (OSNR) and chromatic dispersion (CD). Two transformation methods, the two-dimensional (2D) discrete Fourier transform (DFT) and 2D discrete cosine transform (DCT), are applied to the IQH to extract features with a considerably reduced dimensionality. For the purpose of simulation, the OPM of a 7 × 20 Gbaud dual-polarization–quadrature phase shift keying (DP-QPSK) is considered. Simulations reveal that it can accurately estimate the various optical parameters (i.e., OSNR and CD) with a coefficient of determination value greater than 0.98. In addition, the effectiveness of proposed OPM scheme is examined under different values of polarization mode dispersion and frequency offset, as well as the utilization of different higher order modulation formats. Moreover, proof-of-concept experiments are performed for validation. The results show an excellent matching between the simulation and experimental findings.

With the rapid development of optical communication systems, more advanced techniques conventionally used in long-haul transmissions have gradually entered systems covering shorter distances below 100 km, where higher-speed connections are required in various applications, such as the optical access networks, inter- and intra-data center interconnects, mobile fronthaul, and in-building and indoor communications. One of the techniques that has attracted intensive interests in short-reach optical communications is machine learning (ML). Due to its robust problem-solving, decision-making, and pattern recognition capabilities, ML techniques have become an essential solution for many challenging aspects. In particular, taking advantage of their high accuracy, adaptability, and implementation efficiency, ML has been widely studied in short-reach optical communications for optical performance monitoring (OPM), modulation format identification (MFI), signal processing and in-building/indoor optical wireless communications. Compared with long-reach communications, the ML techniques used in short-reach communications have more stringent complexity and cost requirements, and also need to be more sensitive. In this paper, a comprehensive review of various ML methods and their applications in short-reach optical communications are presented and discussed, focusing on existing and potential advantages, limitations and prospective trends.

Real-time optical performance monitoring (OPM) is of the utmost importance in adaptive optical networks to enable awareness of channel conditions and to achieve high quality of service. In single-mode fiber (SMF)-based networks, optical signal-to-noise ratio (OSNR) and chromatic dispersion (CD) monitoring have been extensively studied in the literature. In this work, we consider OPM in few-mode fiber (FMF) networks employing non-coherent detection. OPM in such networks is a challenging task, as FMF has an additional performance-limiting impairment over SMF, namely mode coupling (MC). Here, we propose an OPM scheme to estimate three FMF channel parameters: OSNR within the range of 8 to 20 dB, CD within the range of 160 to 1120 ps/nm, and different levels of MC. The proposed scheme uses a stacked auto-encoder (AE) to extract features with reduced dimensionality compared to the original data. These features are used to train an artificial neural network (ANN) regressor. Simulation results show that the proposed OPM scheme can accurately estimate the OSNR, CD, and MC with root mean square error (RMSE) values of 0.0015 dB, 0.28 ps/nm, and 7.88 × 10−6, respectively. The performance of proposed OPM scheme is also evaluated against different types of features commonly used in literature.

This paper considers Godard's error as signal quality metric to monitor chromatic dispersion (CD), nonlinear parameter and modulation format in the DSP module of the coherent receivers. We first review a CD monitoring based on Godard's error that can be able to accurately monitor arbitrarily large dispersion values in uncompensated transmission links in combination with frequency domain equalizer, then extend the previous nonlinear parameter monitoring method based on Godard's error by blindly obtaining the optimized value γξp to significantly improve the adaptive capability, and present a simple and effective modulation format monitoring based on Godard's error. Meanwhile, the effectiveness has been experimentally verified in 128-Gb/s PDM-QPSK, 192-Gb/s PDM-8QAM, and 256-Gb/s PDM-16QAM systems.

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.

With the development of 5G technology, high definition video and internet of things, the capacity demand for optical networks has been increasing dramatically. To fulfill the capacity demand, low-margin optical network is attracting attentions. Therefore, planning tools with higher accuracy are needed and accurate models for quality of transmission (QoT) and impairments are the key elements to achieve this. Moreover, since the margin is low, maintaining the reliability of the optical network is also essential and optical performance monitoring (OPM) is desired. With OPM, controllers can adapt the configuration of the physical layer and detect anomalies. However, considering the heterogeneity of the modern optical network, it is difficult to build such accurate modeling and monitoring tools using traditional analytical methods. Fortunately, data-driven artificial intelligence (AI) provides a promising path. In this paper, we firstly discuss the requirements for adopting AI approaches in optical networks. Then, we review various recent progress of AI-based QoT/impairments modeling and monitoring schemes. We categorize these proposed methods by their functions and summarize advantages and challenges of adopting AI methods for these tasks. We discuss the problems remained for deploying AI-based methods to a practical system and present some possible directions for future investigation.

A novel technique is proposed to implement optical signal-to-noise ratio (OSNR) estimation by using an improved binary particle swarm optimization (IBPSO) and deep neural network (DNN) based on amplitude histograms (AHs) of signals obtained after constant modulus algorithm (CMA) equalization in an optical coherent system. For existing OSNR estimation models of DNN and AHs, sparse AHs with valid features of original data are selected by IBPSO algorithm to replace the original, and the sparse sets are used as input vector to train and test the particle swarm optimization (PSO) optimized DNN (PSO-DNN) network structure. Numerical simulations have been carried out in the OSNR ranges from 10 dB to 30 dB for 112 Gbps PM-RZ-QPSK and 112 Gbps PM-NRZ-16QAM signals, and results show that the proposed algorithm achieves a high OSNR estimation accuracy with the maximum estimation error is less than 0.5 dB. In addition, the simulation results with different data input into the deep neural network structure show that the mean OSNR estimation error is 0.29 dB and 0.39 dB under original data and 0.29 dB and 0.37 dB under sparse data for the two signals, respectively. In the future dynamic optical network, it is of more practical significance to reconstruct the original signal and analyze the data using sparse observation information in the face of multiple impairment and serious interference. The proposed technique has the potential to be applied for optical performance monitoring (OPM) and is helpful for better management of optical networks.

We propose and demonstrate a chromatic dispersion (CD) monitoring technique for a nonreturn-to-zero differential phase-shift keying (NRZ-DPSK) signal generated with a Mach-Zehnder modulator at 40 Gb/s by analyzing the waveform overshoot after transmission. Without demodulating the NRZ-DPSK signal, the CD up to 120 ps/nm is monitored experimentally via the amplitude of the overshoot. Furthermore, based on the asynchronous amplitude histogram (AAH) evaluation method, a novel histogram processing method, which is more convenient, is proposed and investigated. Experimental results show that the monitoring scheme for CD is independent of optical signal-to-noise ratio (OSNR) within a certain range. This technique needs neither demodulating the signal nor extracting the synchronous clock, and it can be applied to high-order modulation formats at different bit rates.