Current Optics and Photonics

Optical Society of Korea (한국광학회)
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Single Logarithmic Amplification and Deep Learning-based Fixed-threshold On-off Keying Detection for Free-space Optical Communication

  • Qian-Wen Jing (School of Information Science and Engineering, Shenyang University of Technology) ;
  • Yan-Qing Hong (School of Information Science and Engineering, Shenyang University of Technology)
  • Received : 2024.01.25
  • Accepted : 2024.04.05
  • Published : 2024.06.25
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Abstract

This paper proposes single logarithmic amplification (single-LA) and deep learning (DL)-based fixed-threshold on-off keying (OOK) detection for free-space optical (FSO) communication. Multilevel LAs (MLAs) can be used to mitigate intensity fluctuations in the received OOK signal by their nonlinear gain characteristics; however, it is ineffective in the case of high scintillation, owing to degradation of the OOK signal's extinction ratio. Therefore, a DL technique is applied to realize effective scintillation compensation in single-LA applications. Fully connected (FC) networks and fully connected neural networks (FCNN), which have nonlinear modeling characteristics, are deployed in this work. The performance of the proposed method is evaluated through simulations under various scintillation effects. Simulation results show that the proposed method outperforms the conventional adaptive-threshold-decision, single-LA-based, MLA-based, FC-based, and FCNN-based OOK detection techniques.

Keywords

Acknowledgement

Department of Education of Liaoning Province (Grant no. JYTMS20231214); the Department of Science and Technology of Liaoning Province (Grant no. 2023JH2/101300225).

References

  1. H. Kaushal and G. Kaddoum, "Optical communication in space: Challenges and mitigation techniques," IEEE Commun. Surv. Tutor. 19, 57-96 (2017).
  2. N. Blaunstein, S. Arnon, N. Kopeika, and A. Zilberman, Applied Aspects of Optical Communication and LIDAR, (CRC Press, USA, 2009).
  3. A. S. Hamza, J. S. Deogun, and D. R. Alexander, "Classification framework for free space optical communication links and systems," IEEE Commun. Surv. Tutor. 21, 1346-1382 (2019).
  4. S. Constantine, L. E. Elgin, M. L. Stevens, J. A. Greco, K. Aquino, D. D. Alves, and B. S. Robinson, "Design of a high-speed space modem for the lunar laser communications demonstration," Proc. SPIE 7923, 792308 (2011).
  5. L. Li, T. Geng, Y. Wang, X. Li, J. Wu, Y. Li, S. Ma, S. Gao, and Z. Wu, "Free-space optical communication using coherent detection and double adaptive detection thresholds," IEEE Photon. J. 11, 7900217 (2019).
  6. C. Abou-Rjeily, "Performance analysis of FSO communications with diversity methods: Add more relays or more apertures," IEEE J. Sel. Areas Commun. 33, 1890-1902 (2015).
  7. T. Song and P. Kam, "A robust GLRT receiver with implicit channel estimation and automatic threshold adjustment for the free space optical channel with IM/DD," J. Light. Technol. 32, 369-383 (2014).
  8. Y.-Q. Hong, W.-H. Shin, and S.-K. Han, "Performance of scintillation mitigation for linear polarization shift on-off keying transmission in free-space optical communications," IEEE Access 8, 128954-128960 (2020).
  9. T. Young, D. Hazarika, S. Poria, and E. Cambria, "Recent trends in deep learning based natural language processing," IEEE Comput. Intell. Mag. 13, 55-75 (2018).
  10. X. Wang, T. Wu, C. Dong, H. Zhu, Z. Zhu, and S. Zhao, "Integrating deep learning to achieve phase compensation for free-space orbital-angular-momentum-encoded quantum key distribution under atmospheric turbulence," Photonics Re. 9, B9-B17 (2021).
  11. Z. Du, H. Deng, Y. Dai, Y. Hua, B. Jia, Z. Qian, J. Xiong, W. Lyu, Z. Zhang, D. Ma, and J. Xu, "Experimental demonstration of an OFDM-UWOC system using a direct decoding FCDNN-based receiver," Opt. Commun. 508, 127785 (2022).
  12. M. A. Amirabadi, "A deep learning based solution for imperfect CSI problem in correlated FSO communication channel," arXiv preprint arXiv:1909.11002 (2019).
  13. P.-Z. Yu, Q.-W. Jing, and Y.-Q. Hong, "Multi-level logarithmic amplification-based fixed threshold circular polarized on-off keying detection for free-space optical communications," Photonics 10, 667 (2023).
  14. W.-S. Tsai, H.-H. Lu, Y. Huang, S. Tu, and Q. Huang, "A PDM-based bi-directional fibre-FSO integration with two RSOAs scheme," Sci. Rep. 9, 8317 (2019).
  15. R. T. Jones, T. A. Eriksson, M. P. Yankov, and D. Zibar, "Deep learning of geometric constellation shaping including fiber nonlinearities," in Proc. 2018 European Conference on Optical Communication-ECOC (Rome, Italy, Sep. 23-27, 2018), pp. 1-3.
  16. T. O'Shea and J. Hoydis, "An introduction to deep learning for the physical layer," IEEE Trans. Cogn. Commun. Netw. 3, 563-575 (2017).
  17. J. Long, E. Shelhamer, and T. Darrell, "Fully convolutional networks for semantic segmentation," in Proc. IEEE Conference on Computer Vision and Pattern Recognition (Boston, Massachusetts, USA, Jun. 8-10, 2015), pp. 3431-3440.
  18. S. Ding, J. Zhang, and A. Dang, "Adaptive threshold decision for on-off keying transmission systems in atmospheric turbulence," Opt. Express 25, 24425-24436 (2017).
  19. H. Shen, L. Yu, and C. Fan, "Temporal spectrum of atmospheric scintillation and the effects of aperture averaging and time averaging," Opt. Commun. 330, 160-164 (2014).
  20. Y.-Q. Hong, G. Li, and Z.Y. Liu, "Optical adaptive power transmission using APC-EDFA for turbulence-tolerant FSO communications," Opt. Express 29, 23777-23785 (2021).