Underwater Modulation Classification Using Discrete Wavelet Transform and Genetic Algorithm

##plugins.themes.academic_pro.article.sidebar##

Published Jun 24, 2025
https://doi.org/10.56158/jpte.2025.124.4.01
Download
FULL ARTICLE - PDF
Statistic
Vol. 4 No. 01 (2025): June

##plugins.themes.academic_pro.article.main##

Ali Çimen
Department of Electrical and Electronics Engineering, Engineering Faculty, Amasya University, Amasya, Türkiye.
https://orcid.org/0009-0005-6566-9244
Erdoğan Aldemir
Department of Electronics and Communication, Technical Sciences Vocational School, Batman University, Batman, Türkiye.
https://orcid.org/0000-0003-4772-8317
Timur Düzenli
Department of Electrical and Electronics Engineering, Engineering Faculty, Amasya University, Amasya, Türkiye.
https://orcid.org/0000-0003-0210-5626

Abstract

Underwater wireless optical communication systems face significant challenges due to the heterogeneous nature of the underwater environment and the attenuation of optical signals caused by absorption and scattering. These effects restrict the data transfer capacity and transmission distance, resulting in communication errors. Different modulation techniques are used to minimize the effects of these parameters. Automatic modulation classification plays a critical role in terms of effective management of spectrum resources. In this study, underwater wireless optical communication channels are modulated with different modulation techniques, and the signals are transformed into the discrete wavelet space, resulting in approximation and detail coefficients that are used as feature vectors for training machine learning algorithms. In addition, optimized classification features are determined for different signal-to-noise ratios and different transmission distances using the genetic algorithm. The results show that the approximation and detail coefficient energies provide higher classification performance in the classification of modulated signals according to statistical features such as mean, variance, and standard deviation. According to simulation results, an average classification accuracy of 82% has been obtained using the proposed discrete wavelet transform and genetic algorithm-based technique, which demonstrates high classification accuracy for noisy underwater channels.

##plugins.themes.academic_pro.article.details##

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

How to Cite
Çimen, A., Aldemir, E., & Düzenli, T. (2025). Underwater Modulation Classification Using Discrete Wavelet Transform and Genetic Algorithm. International Journal of Pioneering Technology and Engineering, 4(01), 43–49. https://doi.org/10.56158/jpte.2025.124.4.01

References

  1. Zhu, S., Chen, X., Liu, X., Zhang, G., & Tian, P. (2020). Recent progress in and perspectives of underwater wireless optical communication. Progress in Quantum Electronics, 73, 100274.
  2. Miramirkhani, F., & Uysal, M. (2018). Visible Light Communication Channel Modeling for Underwater Environments With Blocking and Shadowing. IEEE Access, 6, 1082–1090.
  3. Fukumoto, H., Okumura, R., Fujino, Y., Ohmori, S., Ito, Y., Ishihara, T., & Tabata, Y. (2023). Implementation of Bidirectional High-Rate Underwater Acoustic Communication Systems for Fully Wireless-Controlled Remotely Operated Vehicles. In 2023 XXXVth General Assembly and Scientific Symposium of the International Union of Radio Science (URSI GASS) (pp. 1–4). IEEE.
  4. Roy, S., Duman, T. M., McDonald, V., & Proakis, J. G. (2007). High-Rate Communication for Underwater Acoustic Channels Using Multiple Transmitters and Space–Time Coding: Receiver Structures and Experimental Results. IEEE Journal of Oceanic Engineering, 32(3), 663–688.
  5. Che, X., Wells, I., Dickers, G., Kear, P., & Gong, X. (2010). Re-evaluation of RF electromagnetic communication in underwater sensor networks. IEEE Communications Magazine, 48(12), 143–151.
  6. Palmeiro, A., Martin, M., Crowther, I., & Rhodes, M. (2011). Underwater radio frequency communications. In OCEANS 2011 IEEE - Spain (pp. 1–8). IEEE.
  7. Saeed, N., Celik, A., Al-Naffouri, T. Y., & Alouini, M.-S. (2019). Underwater optical wireless communications, networking, and localization: A survey. Ad Hoc Networks, 94, 101935.
  8. Kaushal, H., & Kaddoum, G. (2016). Underwater Optical Wireless Communication. IEEE Access, 4, 1518–1547.
  9. Gabriel, C., Khalighi, M.-A., Bourennane, S., Leon, P., & Rigaud, V. (2011). Channel modeling for underwater optical communication. In 2011 IEEE GLOBECOM Workshops (GC Wkshps) (pp. 833–837). IEEE.
  10. Jamali, M. V., Nabavi, P., & Salehi, J. A. (2018). MIMO Underwater Visible Light Communications: Comprehensive Channel Study, Performance Analysis, and Multiple-Symbol Detection. IEEE Transactions on Vehicular Technology, 67(9), 8223–8237.
  11. Mobley, C. D., Gentili, B., Gordon, H. R., Jin, Z., Kattawar, G. W., Morel, A., … Stavn, R. H. (1993). Comparison of numerical models for computing underwater light fields. Applied Optics, 32(36), 7484.
  12. Spagnolo, G. S., Cozzella, L., & Leccese, F. (2020). Underwater Optical Wireless Communications: Overview. Sensors, 20(8), 2261.
  13. Elfikky, A., Boghdady, A. I., Mumtaz, S., Elsayed, E. E., Singh, M., Abd El-Mottaleb, S. A., … Aly, M. H. (2024). Underwater visible light communication: recent advancements and channel modeling. Optical and Quantum Electronics, 56(10), 1617.
  14. Xing, F., Yin, H., Ji, X., & Leung, V. C. M. (2020). Joint Relay Selection and Power Allocation for Underwater Cooperative Optical Wireless Networks. IEEE Transactions on Wireless Communications, 19(1), 251–264.
  15. Swami, A., & Sadler, B. M. (2000). Hierarchical digital modulation classification using cumulants. IEEE Transactions on Communications, 48(3), 416–429.
  16. Chauhan, D. S., Kaur, G., & Kumar, D. (2023). Deep Learning Based Modulation Classification for Underwater Optical Wireless Communication System. In 2023 International Conference on Emerging Research in Computational Science (ICERCS) (pp. 1–6). IEEE.
  17. Elsayed, E. E., & Yousif, B. B. (2020). Performance enhancement of hybrid diversity for M-ary modified pulse-position modulation and spatial modulation of MIMO-FSO systems under the atmospheric turbulence effects with geometric spreading. Optical and Quantum Electronics, 52(12), 508.
  18. Falconer, D. D., & Foschini, G. J. (1973). Theory of Minimum Mean-Square-Error QAM Systems Employing Decision Feedback Equalization. Bell System Technical Journal, 52(10), 1821–1849.
  19. Guler, E., Hamilton, A., & Popoola, W. (2021). Subcarrier Intensity Modulation for Turbulent Underwater Optical Wireless Communications (pp. 1–2). Institute of Electrical and Electronics Engineers.
  20. Huan Liu, & Lei Yu. (2005). Toward integrating feature selection algorithms for classification and clustering. IEEE Transactions on Knowledge and Data Engineering, 17(4), 491–502.
  21. Jenke, R., Peer, A., & Buss, M. (2014). Feature Extraction and Selection for Emotion Recognition from EEG. IEEE Transactions on Affective Computing, 5(3), 327–339.
  22. Wu, Z., Ren, G., Wang, X., & Zhao, Y. (2004). Automatic Digital Modulation Recognition Using Wavelet Transform and Neural Networks (pp. 936–940).
  23. Ustundag, M. (2021). A Novel Analog Modulation Classification: Discrete Wavelet Transform-Extreme Learning Machine (DWT-ELM). Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 10(2), 492–506.
  24. Sandeep Kaur, Gaganpreet Kaur, & Dheerendra Singh. (2013). Comparative Analysis of Haar and Coiflet Wavelets Using Discrete Wavelet Transform in Digital Image Compression. International Journal of Engineering Research and Applications (IJERA), 3, 669–673.
  25. Cohen, A., Daubechies, I., & Feauveau, J. ‐C. (1992). Biorthogonal bases of compactly supported wavelets. Communications on Pure and Applied Mathematics, 45(5), 485–560.
  26. Kumari, S., & Vijay, R. (2011). Analysis of Orthogonal and Biorthogonal Wavelet Filters for Image Compression. International Journal of Computer Applications, 21(5), 17–19.
  27. Abhinav Dixit, & Swatilekha Majumdar. (2013). Comparative Analysis of Coiflet and Daubechies Wavelets Using Global Threshold for Image De-Noising. International Journal of Advances in Engineering & Technology (IJAET), 6(5), 2247–2252.
  28. Srinivas, M., & Patnaik, L. M. (1994). Genetic algorithms: a survey. Computer, 27(6), 17–26.
  29. Jain, A., & Zongker, D. (1997). Feature selection: evaluation, application, and small sample performance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(2), 153–158.
  30. Urbanowicz, R. J., Meeker, M., La Cava, W., Olson, R. S., & Moore, J. H. (2018). Relief-based feature selection: Introduction and review. Journal of Biomedical Informatics, 85, 189–203.