Blind modulation format identification for digital coherent receivers

Bilal, Syed Muhammad; Bosco, Gabriella; Dong, Zhenhua; Lau, Alan Pak Tao; Lu, Chao

doi:10.1364/oe.23.026769

Cited by 67 publications

(33 citation statements)

References 15 publications

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“…Therefore, the additive white Gaussian noise (AWGN) corrupts the signal primarily. Similar to [9], we assume that the OSNR of the received signal is known. Then, the receiver is able to emulate the signals' CDFs of all possible candidate modulation formats with the known OSNR.…”

Section: B Cdf-based MC Algorithmmentioning

confidence: 99%

Modulation Classification Using Received Signal’s Amplitude Distribution for Coherent Receivers

Lin

Eldemerdash

Dobre

et al. 2017

IEEE Photon. Technol. Lett.

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Abstract-In this letter, we propose a modulation classification algorithm which is based on the received signal's amplitude for coherent optical receivers. The proposed algorithm classifies the modulation format from several possible candidates by differentiating the cumulative distribution function (CDF) curves of their normalized amplitudes. The candidate with the most similar CDF to the received signal is selected. The measure of similarity is the minimum average distance between these CDFs. Five commonly used quadrature amplitude modulation formats in digital coherent optical systems are employed. Optical backto-back experiments and extended simulations are carried out to investigate the performance of the proposed algorithm. Results show that the proposed algorithm achieves accurate classification at optical signal-to-noise ratios of interest. Furthermore, it does not require carrier recovery.

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Section: B Cdf-based MC Algorithmmentioning

confidence: 99%

Modulation Classification Using Received Signal’s Amplitude Distribution for Coherent Receivers

Lin

Eldemerdash

Dobre

et al. 2017

IEEE Photon. Technol. Lett.

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“…In recent years, researchers have utilized various technologies to achieve MFI such as signal cumulant and signal power distribution-based method [7,8], peak-to-average-power ratio of received data samples-based methods [9,10], Stokes space-based methods [11][12][13][14][15], and machine learning-based methods [16][17][18][19]. Various OSNR estimation techniques for coherent detection systems have been proposed recently including statistical moments [20], error vector magnitude [21], delay-line interferometer [22,23], Stokes parameters [24], Golay sequences [25], offset filtering, and optical power measurement [26], radio frequency (RF) spectrum [27], and amplitude noise correlation [28] based methods.…”

Section: Introductionmentioning

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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“…Bo investigated and characterized a blind modulation format recognition method by projecting partial received data in Stokes space onto a 2-D plane to plot a converted binary graph [10]. Based on the evaluation of the peak-to-average-power ratio of the incoming data samples, Bilal presented a simple novel digital modulation format identification scheme for coherent optical systems after some independent DSP processing at the receiver [11]. Liu proposed a modulation format identification technique based on the extraction and identification of specific features of received signal power distributions in digital coherent receivers, which successfully identified six modulation formats [12].…”

Section: Introductionmentioning

confidence: 99%

“…Liu proposed a modulation format identification technique based on the extraction and identification of specific features of received signal power distributions in digital coherent receivers, which successfully identified six modulation formats [12]. However, the method described in [10] requires the approximate OSNR and the carrier frequency of the received signals, the method in [11] requires additional hardware components as well as the OSNR of received signals, and the scheme in [12] requires the computation of the received signal power distributions for digital coherent receivers.…”

Section: Introductionmentioning

confidence: 99%

A Simple Joint Modulation Format Identification and OSNR Monitoring Scheme for IMDD OOFDM Transceivers Using K-Nearest Neighbor Algorithm

et al. 2019

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Featured Application: This paper provides a feasible method for modulation format identification and OSNR monitoring. The application of KNN reduces complexity.Abstract: In this study, a joint modulation format identification and optical signal-to-noise ratio (OSNR) monitoring algorithm is proposed and experimentally demonstrated using the k-nearest neighbor algorithm for intensity modulation and direct detection (IMDD) orthogonal frequency division multiplexing (OFDM) systems. A modified amplitude histogram of received signal is employed to serve as the classification feature to simplify the computation. Experimental results show that five common quadrature amplitude modulation (QAM) modulation formats, including 4-QAM, 16-QAM, 32-QAM, 64-QAM and 128-QAM, can be identified under 100% accurate estimation at the received optical power of −11 dBm. Robustness of the proposed scheme to constellation rotation is also experimentally assessed. At the same time, system OSNR monitoring also can be achieved and the average prediction mean square error (MSE) is 0.69 dB 2 , which is similar to that using an artificial neural network. Computational complexity assessment demonstrated that similar performance but less computing resource consumption can be achieved by using the proposed scheme rather than the artificial neural network-based scheme.

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Blind modulation format identification for digital coherent receivers

Cited by 67 publications

References 15 publications

Modulation Classification Using Received Signal’s Amplitude Distribution for Coherent Receivers

Modulation Classification Using Received Signal’s Amplitude Distribution for Coherent Receivers

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

A Simple Joint Modulation Format Identification and OSNR Monitoring Scheme for IMDD OOFDM Transceivers Using K-Nearest Neighbor Algorithm

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