Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

AI based on frequency slicing deep neural network for underwater visible light communication

  • Research Paper
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

Abstract

In this paper, we propose a low-complexity frequency slicing deep neural network (FSDNN) for wide-band signal post-equalization in a 1.2 m underwater visible light communication system. FSDNN and deep neural network (DNN) outperform the least mean square equalizer. Then, by splitting the received signal into two parallel signals using a digital low-pass filter and a high-pass filter, we demonstrate that the FSDNN significantly reduces the complexity of the traditional DNN post-equalizer. Moreover, the complexity of the FSDNN decreases considerably to 11.15% compared with the conventional DNN for a 2.7 Gbit/s wide-band transmitted signal with a similar bit error ratio performance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Zeng Z Q, Fu S, Zhang H H, et al. A survey of underwater optical wireless communications. IEEE Commun Surv Tut, 2017, 19: 204–238

    Article  Google Scholar 

  2. Chi N, Haas H, Kavehrad M, et al. Visible light communications: demand factors, benefits and opportunities. IEEE Wirel Commun, 2015, 22: 5–7

    Article  Google Scholar 

  3. Zhou Y J, Zhu X, Hu F C, et al. Common-anode LED on a Si substrate for beyond 15 Gbit/s underwater visible light communication. Photon Res, 2019, 7: 1019–1029

    Article  Google Scholar 

  4. Zhao Y H, Zou P, Yu W X, et al. Two tributaries heterogeneous neural network based channel emulator for underwater visible light communication systems. Opt Exp, 2019, 27: 22532–22541

    Article  Google Scholar 

  5. Chi N, Hu F C. Nonlinear adaptive filters for high-speed LED based underwater visible light communication. Chin Opt Lett, 2019, 17: 100011

    Article  Google Scholar 

  6. Chi N, Zhao Y, Shi M, et al. Gaussian kernel-aided deep neural network equalizer utilized in underwater PAM8 visible light communication system. Opt Exp, 2018, 26: 26700–26712

    Article  Google Scholar 

  7. Wu F M, Lin C T, Wei C C, et al. Performance comparison of OFDM signal and CAP signal over high capacity RGB-LED-based WDM visible light communication. IEEE Photon J, 2013, 5: 7901507–7901507

    Article  Google Scholar 

  8. Ziemer R E, Tranter W H. Principles of Communications. Hoboken: John Wiley Sons, 2014

    Google Scholar 

  9. Zibar D, Piels M, Jones R, et al. Machine learning techniques in optical communication. J Lightw Technol, 2016, 34: 1442–1452

    Article  Google Scholar 

  10. Khan F N, Lu C, Lau A P T. Machine learning methods for optical communication systems. In: Proceedings of Signal Processing in Photonic Communications, 2017. 3

    Google Scholar 

  11. Li G, Hu F, Zhao Y, et al. Enhanced performance of a phosphorescent white LED CAP 64QAM VLC system utilizing deep neural network (DNN) post equalization. In: Proceedings of IEEE/CIC International Conference on Communications in China (ICCC), Changchun, 2019. 173–176

    Google Scholar 

  12. Osahon I N, Rajbhandari S, Popoola W O. Performance comparison of equalization techniques for SI-POF multi- Gigabit communication with PAM-M and device non-linearities. J Lightw Technol, 2018, 36: 2301–2308

    Article  Google Scholar 

  13. Kaushal H, Kaddoum G. Underwater optical wireless communication. IEEE Access, 2016, 4: 1518–1547

    Article  Google Scholar 

  14. Ali M A A, Mohammed M A. Effect of atmospheric attenuation on laser communications for visible and infrared wavelengths. Al-Nahrain J Sci, 2013, 16: 133–140

    Google Scholar 

  15. Johnson L, Green R, Leeson M. A survey of channel models for underwater optical wireless communication. In: Proceedings of 2013 2nd International Workshop on Optical Wireless Communications (IWOW), 2013. 1–5

    Google Scholar 

  16. Cossu G. Recent achievements on underwater optical wireless communication. Chin Opt Lett, 2019, 17: 100009

    Article  Google Scholar 

  17. Huang X X, Wang Z X, Shi J Y, et al. 1.6 Gbit/s phosphorescent white LED based VLC transmission using a cascaded pre-equalization circuit and a differential outputs PIN receiver. Opt Express, 2015, 23: 22034–22042

    Article  Google Scholar 

  18. Kim J, Konstantinou K. Digital predistortion of wideband signals based on power amplifier model with memory. Electron Lett, 2001, 37: 1417–1418

    Article  Google Scholar 

  19. Ju C, Liu N, Chen X, et al. SSBI mitigation in A-RF-tone-based VSSB-OFDM system with a frequency-domain volterra series equalizer. J Lightw Technol, 2015, 33: 4997–5006

    Article  Google Scholar 

  20. Zhang J W, Yu J J, Li F, et al. 11×5×9.3 Gb/s WDM-CAP-PON based on optical single-side band multi-level multi-band carrier-less amplitude and phase modulation with direct detection. Opt Exp, 2013, 21: 18842–18848

    Article  Google Scholar 

  21. Burse K, Yadav R N, Shrivastava S C. Channel equalization using neural networks: a review. IEEE Trans Syst Man Cybern C, 2010, 40: 352–357

    Article  Google Scholar 

  22. Zhou Y, Zhang J, Wang C, et al. A novel memoryless power series based adaptive nonlinear pre-distortion scheme in high speed visible light communication. In: Proceedings of Optical Fiber Communication Conference and Exhibition (OFC), 2017

    Book  Google Scholar 

  23. Haykin S O. Neural Networks and Learning Machines. Upper Saddle River: Pearson, 2009. 3

    Google Scholar 

Download references

Acknowledgements

This work was partially supported by National Key Research and Development Program of China (Grant No. 2017YFB0403603) and Natural National Science Foundation of China (Grant No. 61925104).

Author information

Authors and Affiliations

  1. Key Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai, 200433, China

    Nan Chi, Fangchen Hu, Guoqiang Li, Chaofan Wang & Wenqing Niu

Authors
  1. Nan Chi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fangchen Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guoqiang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chaofan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wenqing Niu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nan Chi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chi, N., Hu, F., Li, G. et al. AI based on frequency slicing deep neural network for underwater visible light communication. Sci. China Inf. Sci. 63, 160303 (2020). https://doi.org/10.1007/s11432-020-2851-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11432-020-2851-0

Keywords

Search

Navigation