Blind Modulation Format Identification Using the DC Component

Tan, Qingzhao; Yang, Aiying; Guo, Peng

doi:10.1109/jphot.2019.2908658

Cited by 4 publications

(5 citation statements)

References 19 publications

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“…This will make sense when the low-cost laser with wide linewidth is used in short reach optical communication system [20]. Meanwhile, the factor γ can be used for modulation format identification as reported in our previous work [17].…”

Section: Simulation Resultsmentioning

confidence: 86%

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Blind Frequency Offset Estimation Based on Phase Rotation For Coherent Transceiver

et al. 2020

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Conventional fast Fourier transform based frequency offset estimation (FFT-FOE) algorithm is suitable for QPSK and 8/16/64QAM signals. However, due to the nonrectangular distribution of constellation points, the conventional FFT-FOE is not suitable for 32-QAM signal. In this paper, we report a factor γ which can indicate whether the FFT-FOE algorithm can be used for frequency offset estimation for different modulation formats. The FFT-FOE algorithm is hard to apply for frequency offset estimation if the value of γ is low. Fortunately, the value of γ can be increased by digital amplification and phase rotation. Based on the digital amplification and phase rotation, a modified FFT-FOE algorithm is proposed and can realize the frequency offset estimation of 32-QAM signal with only 512 symbols. The bit error ratio (BER) of a 28 Gbaud 32-QAM signal is lower than the soft-decision forward-error correction (SD-FEC) limit with the optical signal-to-noise ratio (OSNR) of 22 dB. The proposed method has better robustness to phase noise. If BER at SD-FEC limit is considered for 28Gbaud 32-QAM signal, the required OSNR is relaxed by 2 dB when the laser linewidth is greater than 5 MHz. This will make sense when the wide-linewidth laser is used in short reach optical communication system. The experimental results from 10 Gbaud 32-QAM system with 300 km fiber transmission indicate that the proposed method can be applied when OSNR is greater than 14 dB. The results demonstrate that the proposed frequency offset estimation scheme is also suitable for QPSK and 8/16/64QAM signals. Thus, the proposed algorithm can perform blind frequency offset estimation for a coherent transceiver.

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Section: Simulation Resultsmentioning

confidence: 86%

“…1 shows that the centroid amplitude of m 4 n for 32-QAM signal was effected by the distribution of the modulated phase. Considering the effect of the modulated phase, the factor γ is defined as in our previous work [17]:…”

Section: Conventional Frequency Offset Estimation Based On Fftmentioning

confidence: 99%

Blind Frequency Offset Estimation Based on Phase Rotation For Coherent Transceiver

et al. 2020

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“…In addition, it is sensitive to pre-defined threshold values. Exploiting the DC component of the received signal for MFI is reported in [135]. In this technique, the ratio of the 4 th power (exponent) of the DC component to that of the received signal without phase rotation is utilized to distinguish the different modulation formats.…”

Section: A Conventional Opm and Mfi Techniquesmentioning

confidence: 99%

“…Besides, HOC features and some other features extracted from the signal amplitude and phase that are acquired coherently have been exploited in [132], [133], [135]. In contrast to Stokes space constellation features, these features are insensitive to ASE noise.…”

Section: B Features Utilized For Opm and Mfimentioning

confidence: 99%

Machine Learning Techniques for Optical Performance Monitoring and Modulation Format Identification: A Survey

Saif

Esmail

Ragheb

et al. 2020

IEEE Commun. Surv. Tutorials

100

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The trade-off between more user bandwidth and quality of service requirements introduces unprecedented challenges to the next generation smart optical networks. In this regard, the use of optical performance monitoring (OPM) and modulation format identification (MFI) techniques becomes a common need to enable the development of next-generation autonomous optical networks, with ultra-low latency and selfadaptability. Recently, machine learning (ML)-based techniques have emerged as a vital solution to many challenging aspects of OPM and MFI in terms of reliability, quality, and implementation efficiency. This paper surveys ML-based OPM and MFI techniques proposed in the literature. First, we address the key advantages of employing ML algorithms in optical networks. Then, we review the main optical impairments and modulation formats being monitored and classified, respectively, using ML algorithms. Additionally, we discuss the current status of optical networks in terms of MFI and OPM. This includes standards, monitoring parameters, and the available commercial products with their limitations. Second, we provide a comprehensive review of the available ML-based techniques for MFI, OPM, and joint MFI/OPM, describing their performance, advantages, and limitations. Third, we give an overview of the exiting ML-based OPM and MFI techniques for the emerging optical networks such as the new fiber-based networks that use future space division multiplexing techniques (e.g. few-mode fiber), the hybrid radioover-fiber networks, and the free space optical networks. Finally, we discuss the open issues, potential future research directions, and recommendations for the potential implementation of MLbased OPM and MFI techniques. Some lessons learned are presented after each section throughout the paper to help the reader identifying the gaps, weaknesses, and strengths in this field.

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“…The DSP configuration of the proposed joint algorithm is shown in Figure 1. After chromatic dispersion (CD) compensation, timing recovery, normalization, constant modulus algorithm (CMA)-based equalization, and frequency-offset (FO) compensation for compensating nearly all linear transmission impairments, signals are mainly affected by amplified spontaneous emission (ASE) noise [29,43,44]. The proposed DBSCAN-based joint algorithm can extract MF and OSNR information which are used in subsequent DSP blocks such as symbol decision, decoding and signal quality characterization.…”

Section: Model Of Mfi and Osnr Estimation Based On Dbscan Algorithmmentioning

confidence: 99%

A Density Clustering Algorithm for Simultaneous Modulation Format Identification and OSNR Estimation

et al. 2020

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In this work, we propose a combined method to implement both modulation format identification (MFI) and optical signal-to-noise ratio (OSNR) estimation, a method based on density-based spatial clustering of applications with a noise (DBSCAN) algorithm. The proposed method can automatically extract the cluster number and density information of samples. The cluster number can be used for MFI and the density information combined with a fourth-order polynomial fitting can correctly estimate OSNR. We verify the feasibility of the method through simulation and conceptual proof experiments. The results show that the MFI can achieve 100% accuracy when the OSNR values are higher than the 7% forward error correction (FEC) thresholds for five commonly used modulation formats (MFs) like polarization division multiplexing (PDM)-QPSK, PDM-8PSK, PDM-16QAM, PDM-32QAM, and PDM-64QAM. Mean absolute OSNR estimation errors are not higher than 1 dB for different signals. There is no additional hardware required, so the proposed method has the ability to be integrated into existing optical performance monitoring systems without burden. Furthermore, the proposed method has the potential to be used in bit-error ratio (BER) calculation, linear, or nonlinear impairments monitoring. We believe that our multifunctional and simple method would be favorable to a future elastic optical network (EON).

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Blind Modulation Format Identification Using the DC Component

Cited by 4 publications

References 19 publications

Blind Frequency Offset Estimation Based on Phase Rotation For Coherent Transceiver

Blind Frequency Offset Estimation Based on Phase Rotation For Coherent Transceiver

Machine Learning Techniques for Optical Performance Monitoring and Modulation Format Identification: A Survey

A Density Clustering Algorithm for Simultaneous Modulation Format Identification and OSNR Estimation

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