Abstract
Automatic Modulation Format Identification(AMFI) is an important issue in Visible Light Communication (VLC). There are several factors that affect the performance of vehicular VLC (V-VLC) systems. To accomplish the required communication quality for different traffic scenarios, It needs adaptive modulation techniques. Adaptive modulation requires modulation format identification at the receiver to avoid the overhead required to determine the proper modulation format. In this paper, we propose an AMFI scheme based on Deep Learning (DL) for Infrastructure-to-Vehicle (I2V) VLC. Our scheme utilizes the Hough transform to project the constellation diagram of the received modulated signals onto another space to extract features efficiently. Hough transform is estimated for eight different VLC modulation formats, including Q/8/16-Phase Shift Keying (PSK) and 4/16/32/64-Quadrature Amplitude Modulation (QAM) at signal to noise ratio (SNR) ranging from 5 to 25 dB. Conventional classifiers such as Alexnet, Sequeeznet and Googlenet are used to perform the task of AMFI. A study of the impact of the weather conditions and the communication distance on the accuracy of the used classifiers is introduced. The obtained results prove that the proposed scheme can identify the modulation format with high classification accuracy up to 100% at lower SNR levels and different weather conditions.
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Data availability
The data that support the findings of this study are available from the corresponding author upon reasonable request. Also, the data related to the channel path loss model in Sect. 2 was presented previously in part of 17th International Symposium on Wireless Communication Systems, ISWCS 2021 (Eldeeb et al. 2021).
References
Aliaberi, A., Sofotasios, P.C., Muhaidat, S.: Modulation schemes for visible light communications. In: 2019 International Conference on Advanced Communication Technologies and Networking (CommNet), IEEE, pp. 1–10 (2019)
Aminuddin, N.S., Ibrahim, M.M., Ali, N.M., et al.: A new approach to highway lane detection by using Hough transform technique. J. Inf. Commun. Technol. 16(2), 244–260 (2017)
Camporez, H., Costa, W., Pontes, M., et al.: Increasing the reach of visible light communication links through constant-envelope OFDM signals. Opt. Commun. 530(129), p. 179 (2023)
Dahri, F.A., Ali, S., Jawaid, M.M.: A review of modulation schemes for visible light communication. Int. J. Comput. Sci. Netw. Secur. 18(2), pp. 117–125 (2018)
Eldeeb, H.B., Miramirkhani, F., Uysal, M.: A path loss model for vehicle-to-vehicle visible light communications. In: 2019 15th International Conference on Telecommunications (ConTEL), IEEE, pp. 1–5 (2019)
Eldeeb, H.B., Elamassie, M., Uysal, M.: Performance analysis and optimization of cascaded i2v and v2v vlc links. In: 2021 17th International Symposium on Wireless Communication Systems (ISWCS), pp. 1–6, https://doi.org/10.1109/ISWCS49558.2021.9562221(2021)
Fuada, S., Pradana, A., Adiono, T., et al.: Demonstrating a real time QAM-16 visible light communications utilizing off-the-shelf hardware. Results Opt. 10(100), 348 (2023)
Ghadimi, N., Yasoubi, E., Akbari, E. et al.: Squeezenet for the forecasting of the energy demand using a combined version of the sewing training-based optimization algorithm. Heliyon 9, no. 6, p. e16827 (2023).
He, J., Zhou, Y., Shi, J., et al.: Modulation classification method based on clustering and Gaussian model analysis for VLC system. IEEE Photonics Technol. Lett. 32(11), 651–654 (2020). https://doi.org/10.1109/LPT.2020.2991125
Jajoo, G., Kumar, Y., Yadav, S.K., et al.: Blind signal modulation recognition through clustering analysis of constellation signature. Expert Syst. Appl. 90, 13–22 (2017)
Kapse, Y.D., Mohani, S.P.: Comparative analysis of OFDM signal on the basis of different modulation techniques. In: 2022 International Conference on Signal and Information Processing (IConSIP), IEEE, pp. 1–5 (2022)
Khan, L.U.: Visible light communication: applications, architecture, standardization and research challenges. Digit. Commun. Netw. 3(2), 78–88 (2017)
Liu, W., Li, X., Yang, C. et al.: Modulation classification based on deep learning for DMT subcarriers in VLC system. In: 2020 Optical Fiber Communications Conference and Exhibition (OFC), IEEE, pp. 1–3 (2020)
Liu, X., Wang, Y., Zhou, F. et al.: Ber analysis for NOMA-enabled visible light communication systems with M-PSK. In: 2018 10th International Conference on Wireless Communications and Signal Processing (WCSP), pp. 1–7. https://doi.org/10.1109/WCSP.2018.8555627 (2018)
Luan, S., Gao, Y., Chen, W., et al.: Automatic modulation classification: decision tree based on error entropy and global-local feature-coupling network under mixed noise and fading channels. IEEE Wirel. Commun. Lett. 11(8), 1703–1707 (2022)
Ma, Y., Gao, M., Zhang, J., et al.: Modulation format identification based on constellation diagrams in adaptive optical OFDM systems. Opt. Commun. 452, 203–210 (2019)
Memedi, A., Dressler, F.: Vehicular visible light communications: a survey. IEEE Commun. Surv. Tut. 23(1), 161–181 (2020)
Mohamed, S.E.D.N., Al-Makhlasawy, R.M., Khalaf, A.A., et al.: Modulation format recognition based on constellation diagrams and the Hough transform. Appl. Opt. 60(30), 9380–9389 (2021)
Mohamed, S.E.D.N., Al-Makhlasawy, R.M., Abdelaziz, M., et al.: Efficient utilization of Hough transform and orthogonal-triangular decomposition for optical wireless modulation format recognition. Appl. Opt. 61(4), 875–883 (2022)
Peng, S., Jiang, H., Wang, H., et al.: Modulation classification based on signal constellation diagrams and deep learning. IEEE Trans. Neural Netw. Learn. Syst. 30(3), 718–727 (2018)
Salmento, M.L.G., Soares, G.M., Alonso, J.M., et al.: A dimmable offline LED driver with OOK-M-FSK modulation for VLC applications. IEEE Trans. Ind. Electron. 66(7), 5220–5230 (2019). https://doi.org/10.1109/TIE.2018.2868022
Sindhubala, K., Vijayalakshmi, B.: Design and performance analysis of visible light communication system through simulation. In: 2015 International Conference on Computing and Communications Technologies (ICCCT), pp. 215–220, https://doi.org/10.1109/ICCCT2.2015.7292748(2015)
Singya, P.K., Shaik, P., Kumar, N., et al.: A survey on higher-order QAM constellations: technical challenges, recent advances, and future trends. IEEE Open J. Commun. Soci. 2, 617–655 (2021)
Sun, H., Zhang, Y., Wang, F., et al.: SVM aided signal detection in generalized spatial modulation VLC system. IEEE Access 9, 80,360-80,372 (2021). https://doi.org/10.1109/ACCESS.2021.3084823
Taha, B., Fayed, H.A., Aly, M.H., et al.: A reduced PAPR hybrid OFDM visible light communication system. Opt. Quant. Electron. 54(12), p. 815 (2022)
Varun, R., Kini, Y.V., Manikantan, K., et al.: Face recognition using Hough transform based feature extraction. Procedia Comput. Sci. 46, 1491–1500 (2015)
Xu, W., Wang, Y., Wang, F. et al.: PSK/QAM modulation recognition by convolutional neural network. In: 2017 IEEE/CIC International Conference on Communications in China (ICCC), pp. 1–5 (2017). https://doi.org/10.1109/ICCChina.2017.8330326
Yashashwi, K., Sethi, A., Chaporkar, P.: A learnable distortion correction module for modulation recognition. IEEE Wirel. Commun. Lett. 8(1), 77–80 (2019). https://doi.org/10.1109/LWC.2018.2855749
Zaiton A, Rahim H, Jasman F, et al Performance characterization of phase shift keying modulation techniques for indoor visible light communication system. AIP Conf. Proc. 8 January 2020; 2203(1), 020023. https://doi.org/10.1063/1.5142115
Zhang, L., Zhou, X., Du, J., et al.: Fast self-learning modulation recognition method for smart underwater optical communication systems. Opt. Express 28(25), 38,223-38,240 (2020a)
Zhang, Q., Hu, G., Zhao, P., & Yang, L.: Modulation recognition of 5G signals based on AlexNet convolutional neural network. In Journal of Physics: Conference Series (Vol. 1453, No. 1, p. 012118). IOP Publishing (2020b)
Zhang, F., Wang, F., Zhang, J., et al.: SVM aided LEDs selection for generalized spatial modulation of indoor VLC systems. Opt. Commun. 497, p. 127161 (2021). https://doi.org/10.1016/j.optcom.2021.127161
Zhao, Y., Vongkulbhisal, J.: Design of visible light communication receiver for on-off keying modulation by adaptive minimum-voltage cancelation. Eng. J. 17, 125–130 (2013)
Zhao, Z., Yang, A., Guo, P., et al.: A modulation format identification method based on amplitude deviation analysis of received optical communication signal. IEEE Photon J. 11(1), 1–7 (2019)
Zhao, Z., Khan, F.N., Li, Y., et al.: Application and comparison of active and transfer learning approaches for modulation format classification in visible light communication systems. Opt. Express 30(10), 16,351-16,361 (2022)
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This work was funded by the Researchers Supporting Project Number (RSP2024R102) King Saud University, Riyadh, Saudi Arabia.
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Arafa, N.A., Lizos, K.A., Alfarraj, O. et al. Deep learning approach for automatic modulation format identification in vehicular visible light communications. Opt Quant Electron 56, 1083 (2024). https://doi.org/10.1007/s11082-024-06987-3
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DOI: https://doi.org/10.1007/s11082-024-06987-3