• Photonics Research
  • Vol. 9, Issue 1, B1 (2021)
Qiang Cai1、†, Ya Guo1、2、†, Pu Li1、3、4、*, Adonis Bogris5, K. Alan Shore6, Yamei Zhang7, and Yuncai Wang3
Author Affiliations
  • 1Key Laboratory of Advanced Transducers and Intelligent Control System, Ministry of Education, Taiyuan University of Technology, Taiyuan 030024, China
  • 2School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710072, China
  • 3School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China
  • 4Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai University, Shanghai 200444, China
  • 5Department of Informatics and Computer Engineering, University of West Attica, Athens 12243, Greece
  • 6School of Electronic Engineering, Bangor University, Wales LL57 1UT, UK
  • 7Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
  • show less
    DOI: 10.1364/PRJ.409114 Cite this Article Set citation alerts
    Qiang Cai, Ya Guo, Pu Li, Adonis Bogris, K. Alan Shore, Yamei Zhang, Yuncai Wang. Modulation format identification in fiber communications using single dynamical node-based photonic reservoir computing[J]. Photonics Research, 2021, 9(1): B1 Copy Citation Text show less

    Abstract

    We present a simple approach based on photonic reservoir computing (P-RC) for modulation format identification (MFI) in optical fiber communications. Here an optically injected semiconductor laser with self-delay feedback is trained with the representative features from the asynchronous amplitude histograms of modulation signals. Numerical simulations are conducted for three widely used modulation formats (on–off keying, differential phase-shift keying, and quadrature amplitude modulation) for various transmission situations where the optical signal-to-noise ratio varies from 12 to 26 dB, the chromatic dispersion varies from -500 to 500 ps/nm, and the differential group delay varies from 0 to 20 ps. Under these situations, final simulation results demonstrate that this technique can efficiently identify all those modulation formats with an accuracy of >95% after optimizing the control parameters of the P-RC layer such as the injection strength, feedback strength, bias current, and frequency detuning. The proposed technique utilizes very simple devices and thus offers a resource-efficient alternative approach to MFI.

    dE(t)dt=1+iα2{g[N(t)N0]1+ε|E(t)|21τp}E(t)+kfτinE(tτ)exp(i2πντ)+kinjτinEinj(t)exp(i2πΔνt)+2βN(t)χ(t),(1)

    View in Article

    dN(t)dt=JN(t)τsg[N(t)N0]1+ε|E(t)|2|E(t)|2,(2)

    View in Article

    Einj(t)=Idexp[iπS(t)],(3)

    View in Article

    Y(n)=XiWi.(4)

    View in Article

    ER=bQ×100%.(5)

    View in Article

    Qiang Cai, Ya Guo, Pu Li, Adonis Bogris, K. Alan Shore, Yamei Zhang, Yuncai Wang. Modulation format identification in fiber communications using single dynamical node-based photonic reservoir computing[J]. Photonics Research, 2021, 9(1): B1
    Download Citation