An Effective Modulation Format Identification Based on Intensity Profile Features for Digital Coherent Receivers

An effective and blind modulation format identification (MFI) scheme based on intensity profile features is proposed for digital coherent receivers of elastic optical networks. Here, different modulation format types present different profile features, which can be regarded as a classification metric. The proposed scheme requires no prior training and is independent of carrier phase noise or frequency offset. Besides, optical signal noise ratio (OSNR) values do not need to be pre-known in the identification process. In this paper, the first step of our scheme is to classify different classes based on different numbers of peak points, and then the second step is to apply height and width ratios between peak points to achieve the final identification process in each class. The feasibility is first demonstrated via numerical simulations in 28GBaud polarization division multiplexing (PDM)-quadrature phase shift keying (QPSK)/-8 quadrature amplitude modulation (QAM)/-16QAM/-32QAM/-64 QAM systems. The results show that the lowest required OSNR values to achieve 100% recognition rate for all modulation formats signals are much lower than the corresponding theoretical 7% forward error correction (FEC) limit (bit error ratio (BER) = 3.8 × 10–3). Furthermore, that of PDM-QPSK/-8QAM/-16QAM signals are even lower than or close to 20% FEC limit (BER = 2.4 × 10–2). Subsequently, the performance is further verified by proof-of-concept experiments among 28GBaud PDM-QPSK/-8QAM/-16QAM, and 21.5GBaud PDM-32QAM systems under back-to-back and long-distance links (from 400 km to 2000 km).

[1]  Yahia A. Eldemerdash,et al.  Modulation Classification Using Received Signal’s Amplitude Distribution for Coherent Receivers , 2017, IEEE Photonics Technology Letters.

[2]  Danijela Cabric,et al.  Energy-Efficient Processor for Blind Signal Classification in Cognitive Radio Networks , 2014, IEEE Transactions on Circuits and Systems I: Regular Papers.

[3]  Idelfonso Tafur Monroy,et al.  Clustering algorithms for Stokes space modulation format recognition. , 2015, Optics express.

[4]  F. Hauske,et al.  DSP for Coherent Single-Carrier Receivers , 2009, Journal of Lightwave Technology.

[5]  Octavia A. Dobre,et al.  On the likelihood-based approach to modulation classification , 2009, IEEE Transactions on Wireless Communications.

[6]  Ming Tang,et al.  RF-pilot aided modulation format identification for hitless coherent transceiver. , 2017, Optics express.

[7]  Masahiko Jinno,et al.  Elastic optical networking: a new dawn for the optical layer? , 2012, IEEE Communications Magazine.

[8]  C. Chatfield,et al.  Fourier Analysis of Time Series: An Introduction , 1977, IEEE Transactions on Systems, Man, and Cybernetics.

[9]  Tianwai Bo,et al.  Robust and Blind Modulation Format Identification for Elastic Optical Networks , 2018, 2018 European Conference on Optical Communication (ECOC).

[10]  Eiji Oki,et al.  Routing and Spectrum Allocation in Elastic Optical Networks: A Tutorial , 2015, IEEE Communications Surveys & Tutorials.

[11]  Valerie A. Thomas,et al.  Fitting the Multitemporal Curve: A Fourier Series Approach to the Missing Data Problem in Remote Sensing Analysis , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Lin Jiang,et al.  Modulation format identification and OSNR monitoring using density distributions in Stokes axes for digital coherent receivers. , 2019, Optics express.

[13]  Tianwai Bo,et al.  Blind Density-Peak-Based Modulation Format Identification for Elastic Optical Networks , 2018, Journal of Lightwave Technology.

[14]  DeWayne R. Derryberry.  Basic Data Analysis for Time Series with R , 2014 .

[15]  Nannan Zhang,et al.  Identifying modulation formats through 2D Stokes planes with deep neural networks. , 2018, Optics express.

[16]  Changjian Guo,et al.  Stokes space modulation format classification based on non-iterative clustering algorithm for coherent optical receivers. , 2017, Optics express.

[17]  Gabriella Bosco,et al.  Blind modulation format identification for digital coherent receivers. , 2015, Optics express.

[18]  S ONGNIAN,et al.  Modulation format identification enabled by the digital frequency-offset loading technique for hitless coherent transceiver , 2018 .

[19]  J. Yu,et al.  Cascaded two-modulus algorithm for blind polarization de-multiplexing of 114-Gb/s PDM-8-QAM optical signals , 2009, 2009 Conference on Optical Fiber Communication - incudes post deadline papers.

[20]  Darko Zibar,et al.  Machine Learning Techniques for Optical Performance Monitoring From Directly Detected PDM-QAM Signals , 2017, Journal of Lightwave Technology.

[21]  Tianwai Bo,et al.  Modulation Format Recognition for Optical Signals Using Connected Component Analysis , 2017, IEEE Photonics Technology Letters.

[22]  Xue Chen,et al.  Modulation Format Recognition and OSNR Estimation Using CNN-Based Deep Learning , 2017, IEEE Photonics Technology Letters.

[23]  Roberto Proietti,et al.  Blind modulation format identification using nonlinear power transformation. , 2017, Optics express.

[24]  Chao Lu,et al.  Signal power distribution based modulation format identification for coherent optical receivers , 2017 .

[25]  Chao Lu,et al.  Modulation Format Identification in Coherent Receivers Using Deep Machine Learning , 2016, IEEE Photonics Technology Letters.

[26]  Elsayed Elsayed Azzouz,et al.  Algorithms for automatic modulation recognition of communication signals , 1998, IEEE Trans. Commun..

[27]  Brian M. Sadler,et al.  Hierarchical digital modulation classification using cumulants , 2000, IEEE Trans. Commun..

[28]  B. Ramkumar,et al.  Automatic modulation classification for cognitive radios using cyclic feature detection , 2009, IEEE Circuits and Systems Magazine.

[29]  S. Savory.  Digital Coherent Optical Receivers: Algorithms and Subsystems , 2010, IEEE Journal of Selected Topics in Quantum Electronics.