Abstract
We propose a graph-based modulation format identification (MFI) scheme for elastic optical network (EON), which exploits the trajectory information of the adjacent received symbols to identify six commonly-used modulation formats signals. A uniform grid is constructed in the first quadrant of two-dimensional (2D) Stokes plane to capture the received symbols sequence, and then the corresponding trajectory information is converted into the adjacent matrix via graph theory. The eigenvector associated with the largest eigenvalue of the adjacent matrix is selected as the discriminated-feature of the corresponding modulation format signal. Subsequently, we employ cosine similarity algorithm to obtain the modulation format of the unknown signal by analyzing the angle between the discriminated-features of the canonical modulation formats signals and the unknown signal. Then, the effectiveness of the proposed MFI scheme is verified through 28 GBaud polarization division multiplexing (PDM)-binary phase shift keying (BPSK), PDM-quadrature phase shift keying (QPSK), PDM-8 quadrature amplitude modulation (QAM), PDM-16QAM, PDM-32QAM, and PDM-64QAM simulation systems. The simulation results show that the proposed MFI scheme achieves well performance on the required minimum optical signal-to-noise ratio (OSNR) value, the robustness to the variation of the transmission environment, residual chromatic dispersion (CD) and different group delay (DGD). Finally, the proposed MFI scheme is further verified by 20 GBaud PDM-QPSK/8QAM/-16QAM/-32QAM long-haul transmission experiments, and the results demonstrate that the proposed MFI scheme exhibits good resilience towards fiber nonlinear impairments.
Highlights
With the increasing demands of simultaneously supporting various data services, such as cloud computing, internet of things and artificial intelligence services, the generation optical transmitter network is envisioned to be more cognitive
The simulation results of six commonly-used modulation formats demonstrate the performance of the proposed modulation format identification (MFI) scheme on the identification accuracy with different values of optical signal-to-noise ratio (OSNR), Residual chromatic dispersion (CD), different group delay (DGD) and robustness with variation of the transmission environment
The 28-Gbaud polarization division multiplexing (PDM)-binary phase shift keying (BPSK), PDM-quadrature phase shift keying (QPSK), PDM-8QAM, PDM-16QAM, PDM-32QAM and PDM64QAM signals are transmitted into back-to-back transmission scenarios through an additive Gaussian white noise channel under variable OSNR conditions, which are generated by a pseudorandom binary sequence (PRBS) with a length of (215 − 1)
Summary
With the increasing demands of simultaneously supporting various data services, such as cloud computing, internet of things and artificial intelligence services, the generation optical transmitter network is envisioned to be more cognitive. The first kind, which was called decision-parameter based type, employed time-domain or frequency-domain features of different standard modulation formats as the identification criteria, such as, density-peak [3], average amplitude deviation [4], intensity profile feature [5], peak-to-average power ratio [6], differential phase distribution ratio and average of amplitude ratio [7], intensity fluctuation feature [8] and high-order cyclic cumulant [9], [10]. For the review of MFI researches above, compared with the decision-parameter based type and aided information based type, machine learning based algorithms have attracted significant attentions with the advantage of flexible and high-performance. It will be necessary to propose a novel MFI algorithm which can be able to identify multi-types modulation formats with lower training samples
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
