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CNN direct equalization in OFDM-VLC systems: evaluations in a numerical model based on experimental characterizations | Photonic Network Communications Skip to main content
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CNN direct equalization in OFDM-VLC systems: evaluations in a numerical model based on experimental characterizations

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Abstract

An investigation on the orthogonal frequency division multiplexing (OFDM) equalization using deep learning architectures for a multipath single-input single-output visible light communication (VLC) channel is presented in this work. Convolution neural networks (CNN) architectures are applied in a direct OFDM mapped symbols equalization, without channel estimation, interpolation nor element-wised division, denominated CNN-Direct Equalization (CNN-DE). The performance analysis of the proposed equalizer is evaluated by considering the mean square error, bit error rate (BER), and error vector magnitude, over different signal-to-noise ratio (SNR) scenarios. Simulation results show that the proposed CNN-DE outperforms the least-square channel estimation (LS) for lower values of SNR (lower than 10 dB), which validates the CNN-DE application for noisy channels. The CNN-DE performs similarly as LS-based equalization, in terms of BER, when the LED non-linear effects and a more realistic VLC channel are taken into consideration.

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References

  1. Rohling, H.: OFDM: Concepts For Future Communication Systems. Springer Science & Business Media, Berlin (2011)

    Book  MATH  Google Scholar 

  2. Kabir, W.: Orthogonal frequency division multiplexing (OFDM). In: 2008 China–Japan Joint Microwave Conference, pp. 178–184 (2008). https://doi.org/10.1109/CJMW.2008.4772401

  3. Kumar, A., NandhaKumar, P.: OFDM system with cyclostationary feature detection spectrum sensing. ICT Express 5(1), 21 (2019)

    Article  Google Scholar 

  4. Zaidi, A.A., Baldemair, R., Moles-Cases, V., He, N., Werner, K., Cedergren, A.: OFDM numerology design for 5G new radio to support IoT, eMBB, and MBSFN. IEEE Commun. Stand. Mag. 2(2), 78 (2018)

    Article  Google Scholar 

  5. Zhang, X., Yuan, Z.: The application of interpolation algorithms in OFDM channel estimation. Int. J. Simul. Syst. Sci. Technol. 17, 11 (2016)

    Google Scholar 

  6. Coleri, S., Ergen, M., Puri, A., Bahai, A.: Channel estimation techniques based on pilot arrangement in OFDM systems. IEEE Trans. Broadcast. 48(3), 223 (2002)

    Article  Google Scholar 

  7. Balevi, E., Andrews, J.G.: Deep Learning-Based Channel Estimation for High-Dimensional Signals. arXiv preprint arXiv:1904.09346 (2019)

  8. Picorone, A.A.M., Oliveira, T.R., Ribeiro, M.V.: PLC channel estimation based on pilots signal for OFDM modulation: a review. IEEE Lat. Am. Trans. 12(4), 580 (2014)

    Article  Google Scholar 

  9. Van De Beek, J.J., Edfors, O., Sandell, M., Wilson, S.K., Borjesson, P.O.: On channel estimation in OFDM systems. In: 1995 IEEE 45th Vehicular Technology Conference. Countdown to the Wireless Twenty-First Century, vol. 2, pp. 815–819 (1995)

  10. Soltani, M., Pourahmadi, V., Mirzaei, A., Sheikhzadeh, H.: Deep learning-based channel estimation. IEEE Commun. Lett. 23(4), 652 (2019). https://doi.org/10.1109/LCOMM.2019.2898944

    Article  Google Scholar 

  11. Ye, H., Li, G.Y., Juang, B.H.: Power of deep learning for channel estimation and signal detection in OFDM systems. IEEE Wirel. Commun. Lett. 7(1), 114 (2017)

    Article  Google Scholar 

  12. Ahmad, S.T., Kumar, K.P.: Radial basis function neural network nonlinear equalizer for 16-QAM coherent optical OFDM. IEEE Photonics Technol. Lett. 28(22), 2507 (2016)

    Article  Google Scholar 

  13. Giacoumidis, E., Le, S., Aldaya, I., Wei, J., McCarthy, M., Doran, N., Eggleton, B.: Experimental comparison of artificial neural network and Volterra based nonlinear equalization for CO-OFDM. In: Optical Fiber Communication Conference, pp. W3A–4 (2016)

  14. Huang, H., Yang, J., Huang, H., Song, Y., Gui, G.: Deep learning for super-resolution channel estimation and DOA estimation based massive MIMO system. IEEE Trans. Veh. Technol. 67(9), 8549 (2018)

    Article  Google Scholar 

  15. Jiang, R., Wang, X., Cao, S., Zhao, J.: Deep neural networks for channel estimation in underwater acoustic OFDM systems. IEEE Access 7, 23579 (2019). https://doi.org/10.1109/ACCESS.2019.2899990

    Article  Google Scholar 

  16. Zhang, Y., Li, J., Zakharov, Y., Li, X., Li, J.: Deep learning based underwater acoustic OFDM communications. Appl. Acoust. 154, 53 (2019). https://doi.org/10.1016/j.apacoust.2019.04.023

    Article  Google Scholar 

  17. Wu, X., Huang, Z., Ji, Y.: Deep neural network method for channel estimation in visible light communication. Opt. Commun. 462, 125272 (2020)

    Article  Google Scholar 

  18. Aoudia, F.A., Hoydis, J.: End-to-end learning for OFDM: from neural receivers to pilotless communication. IEEE Trans. Wirel. Commun. (2021). https://doi.org/10.1109/TWC.2021.3101364

    Article  Google Scholar 

  19. Costa, W.S., Samatelo, J.L., Rocha, H.R., Segatto, M.E., Silva, J.A.: Direct equalization with convolutional neural networks in OFDM based VLC systems. In: 2019 IEEE Latin-American Conference on Communications (LATINCOM), pp. 1–6 (2019)

  20. Lipton, Z.C., Berkowitz, J., Elkan, C.: A critical review of recurrent neural networks for sequence learning. arXiv preprint arXiv:1506.00019 (2015)

  21. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT press, Cambridge (2016)

    MATH  Google Scholar 

  22. Bazgir, O., Zhang, R., Dhruba, S.R., Rahman, R., Ghosh, S., Pal, R.: Representation of features as images with neighborhood dependencies for compatibility with convolutional neural networks. Nat. Commun. 11(1), 1 (2020)

    Article  Google Scholar 

  23. Chen, W., Qi, L., Yanjun, F.: An improved least square channel estimation algorithm for underwater acoustic OFDM systems. In: 2010 2nd International Conference on Future Computer and Communication, vol. 3, pp. V3–577 (2010)

  24. Ghassemlooy, Z., Popoola, W., Rajbhandari, S.: Optical Wireless Communications: System and Channel Modelling with Matlab®. CRC Press, Boca Raton (2019)

    Book  Google Scholar 

  25. Monteiro, F.T., Costa, W.S., Neves, J.L., Silva, D.M., Rocha, H.R., Salles, E.O., Silva, J.A.: Experimental evaluation of pulse shaping based 5G multicarrier modulation formats in visible light communication systems. Opt. Commun. 457, 124693 (2020)

    Article  Google Scholar 

  26. Costa, W., Camporez, H., Segatto, M., Rocha, H., Silva, J.: Towards AI-enhanced VLC systems. In: Optical Fiber Communication Conference, pp. W3I–7 (2022)

  27. Qiu, Y., Chen, H.H., Meng, W.X.: Channel modeling for visible light communications—a survey. Wirel. Commun. Mob. Comput. 16(14), 2016 (2016)

    Article  Google Scholar 

  28. vd Zwaag, K.M., Neves, J.L., Rocha, H.R., Segatto, M.E., Silva, J.A.: Adaptation to the LEDs flicker requirement in visible light communication systems through CE-OFDM signals. Opt. Commun. 441, 14 (2019)

    Article  Google Scholar 

  29. Aloysius, N., Geetha, M.: A review on deep convolutional neural networks. In: 2017 International Conference on Communication and Signal Processing (ICCSP), pp. 0588–0592 (2017)

  30. O’Shea, K., Nash, R.: An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458 (2015)

  31. Kiranyaz, S., Ince, T., Abdeljaber, O., Avci, O., Gabbouj, M.: 1-d convolutional neural networks for signal processing applications. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8360–8364 (2019)

  32. Kiranyaz, S., Avci, O., Abdeljaber, O., Ince, T., Gabbouj, M., Inman, D.J.: 1d convolutional neural networks and applications: a survey. Mech. Syst. Signal Process. 151, 107398 (2021)

    Article  Google Scholar 

  33. Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)

  34. Nagi, J., Ducatelle, F., Di Caro, G.A., Cireşan, D., Meier, U., Giusti, A., Nagi, F., Schmidhuber, J., Gambardella, L.M.: Max-pooling convolutional neural networks for vision-based hand gesture recognition. In: 2011 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), pp. 342–347 (2011)

  35. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

  36. Shafik, R.A., Rahman, M.S., Islam, A.R., Ashraf, N.S.: On the error vector magnitude as a performance metric and comparative analysis. In: 2006 International Conference on Emerging Technologies, pp. 27–31 (2006)

  37. Yu, Z., Baxley, R.J., Zhou, G.T.: EVM and achievable data rate analysis of clipped OFDM signals in visible light communication. EURASIP J. Wirel. Commun. Netw. 2012(1), 321 (2012)

    Article  Google Scholar 

  38. Caruso, C., Quarta, F.: Interpolation methods comparison. Comput. Math. Appl. 35(12), 109 (1998)

    Article  MATH  Google Scholar 

  39. Zwaag, K.M.V.D., Marinho, M.P., Costa, W.D.S., De Assis Souza Dos Santos, F., Bastos-Filho, T.F., Rocha, H.R.O., Segatto, M.E.V., Silva, J.A.L.: A Manchester-OOK visible light communication system for patient monitoring in intensive care units. IEEE Access 9, 104217 (2021). https://doi.org/10.1109/ACCESS.2021.3099462

    Article  Google Scholar 

  40. Elgala, H., Mesleh, R., Haas, H.: An LED model for intensity-modulated optical communication systems. IEEE Photonics Technol. Lett. 22(11), 835 (2010)

    Article  Google Scholar 

  41. Foody, G.M.: Status of land cover classification accuracy assessment. Remote Sens. Environ. 80(1), 185 (2002)

    Article  Google Scholar 

  42. Kłopotek, M. A., Wierzchoń, S. T., Trojanowski, K. (2004). (Eds.), Intelligent Information Processing and Web Mining. Springer Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39985-8

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Acknowledgements

This work was partially supported by FAPES, CNPq and CAPES in the projects FAPES-2021-WMR44, NEsT-5 G-84343540, CNPq 309737/2021-4, 309490/2021-9 and OWIND, during a scholarship supported by the International Cooperation Program PIPC at the Technische Universität Berlin. Financed by Capes - Brazilian Federal Agency for Support and Evaluation of Graduate Education within the Ministry of Education of Brazil.

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  1. LabTel - Telecommunication Laboratory, Department of Electrical Engineering, Federal University of Espírito Santo, Vitória, Brazil

    Wesley S. Costa, Jorge L. A. Samatelo, Helder R. O. Rocha, Marcelo E. V. Segatto & Jair A. L. Silva

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  1. Wesley S. Costa
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  2. Jorge L. A. Samatelo
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Costa, W.S., Samatelo, J.L.A., Rocha, H.R.O. et al. CNN direct equalization in OFDM-VLC systems: evaluations in a numerical model based on experimental characterizations. Photon Netw Commun 45, 1–11 (2023). https://doi.org/10.1007/s11107-022-00987-7

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