Chinese Journal of Lasers, Volume. 49, Issue 18, 1806001(2022)

Secure Communication via Laser Chaos Synchronization Based on Reservoir Computing

Jiayue Liu1,2, Jianguo Zhang1,2、*, Chuangye Li1,2, and Yuncai Wang3,4
Author Affiliations
  • 1Key Laboratory of Advanced Transducers and Intelligent Control System, Ministry of Education, Taiyuan University of Technology, Taiyuan 030024, Shanxi, China
  • 2College of Physics and Optoelectronics, Taiyuan University of Technology, Taiyuan 030024, Shanxi, China
  • 3Guangdong Provincial Key Laboratory of Photonics Information Technology, Guangzhou 510006, Guangdong, China
  • 4School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, Guangdong, China
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    Figures & Tables(11)
    Schematic of laser chaos synchronization communication system based on reservoir computing (RC). C(t): laser chaotic carrier; M(t): original message; n(t): noise; C(t)+M(t): encrypted signal; C′(t): synchronized chaotic carrier; M′(t): decrypted message; u(t): input variables of RC; y(t): predicted output variables of RC; LD1/LD2:laser diode; ISO: optical isolator; MZM: Mach–Zehnder modulator; Message: useful message; VOA: variable optical attenuator; OC1/OC2:optical coupler; ADC: analogue-to-digital converter; PD1/PD2:photodetector
    Principle diagram of reservoir computing
    Schematics of model-free prediction and cross-prediction. (a) Model-free prediction; (b) cross-prediction
    Temporal waveforms when mask coefficient α is 5.56% and signal-to-noise ratio RSN is 20 dB. (a) Original message; (b) chaotic carrier; (c) encrypted signal
    Carrier synchronization results when mask coefficient α is 5.56% and signal-to-noise ratio RSN is 20 dB. (a) Temporal waveforms of target carrier and predicted carrier, as well as the corresponding synchronization error, the inset shows the detail of prediction error; (b) chaotic synchronization plot of predicted synchronization carrier by RC and target carrier received from transmitter
    Temporal waveforms of message decryption when mask coefficient α is 5.56% and signal-to-noise ratio RSN is 20 dB. (a) Original message and quantized recovered message; (b) original message with noise and recovered message before quantizing
    BER, NMSE and synchronization coefficient β versus reservoir nodes N when mask coefficient α is 5.56% and RSN is 20 dB. (a) N ranges from 1000 to 3000 in intervals of 200; (b) enlarged details (N ranges from 1000 to 3000 in intervals of 200)
    NMSE and BER of RC decryption and BER of direct linear filtering (DLF) attack versus RSN for the case with mask coefficient α of 5.56%
    Temporal waveforms of carrier synchronization and the corresponding SMSE when mask coefficient α=2.23%, signal-to-noise ratio RSN=20 dB, training sample quantity is 20000, and testing sample quantity is 3000. (a1)(a2) Temporal waveforms of carrier synchronization; (b1)(b2) SMSE
    Original images, encrypted images, images of DLF attack, and RC decryption images when mask coefficient α is 2.23%, 4.45%, and 6.68%, respectively
    • Table 1. Values of parameters used in simulation

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      Table 1. Values of parameters used in simulation

      Symbol and unitParameterValue
      αLinewidth enhancement factor3.5
      GDifferential gain coefficient7.0×103
      N0Transparent carrier density1.5×108
      τp /psPhoton lifetime2.0
      τn /nsCarrier lifetime2.0
      τ /nsRound-trip time of light in the external cavity5
      Ith /mAThreshold current17.7
      V /m3Active area volume of the laser1.2×10-16
      q /CCharge quantity1.6×10-19
      kFeedback coefficient3.5×1010
      λ /nmOperation wavelength of LD1 and LD21550
      h /psTime stepping2.5
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    Jiayue Liu, Jianguo Zhang, Chuangye Li, Yuncai Wang. Secure Communication via Laser Chaos Synchronization Based on Reservoir Computing[J]. Chinese Journal of Lasers, 2022, 49(18): 1806001

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    Paper Information

    Category: Fiber optics and optical communication

    Received: Dec. 1, 2021

    Accepted: Jan. 20, 2022

    Published Online: Aug. 10, 2022

    The Author Email: Zhang Jianguo (zhangjianguo@tyut.edu.cn)

    DOI:10.3788/CJL202249.1806001

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