Analysis of the Performance for Quality of Transmission in Optical Fiber Communication Based on Machine Learning Optimization

References

[1] Valsalan, P., Hasan, N. U., Baig, I., Zghaibeh, M., Farooq, U., & Suhail, S. (2024). Unleashing the potential: The joint of 5G and 6G technologies in enabling advanced IoT communication and sensing systems: A comprehensive review and future prospects. J. Commun, 19(11), 523-535.

[2] Garg, D., & Nain, A. (2023). Next generation optical wireless communication: a comprehensive review. Journal of Optical Communications, 44(s1), s1535-s1550.

[3] Pham, T. L., Shahjalal, M. D., Bui, V., & Jang, Y. M. (2020). Deep learning for optical vehicular communication. IEEE access, 8, 102691-102708.

[4] Yeh, C. H., Liu, L. H., Lin, W. P., Ko, H. S., Lai, Y. T., & Chow, C. W. (2022). A survivable optical network for WDM access against fiber breakpoint. IEEE Access, 10, 25828-25833.

[5] Casellas, R., Martínez, R., Vilalta, R., & Muñoz, R. (2018). Control, management, and orchestration of optical networks: evolution, trends, and challenges. Journal of Lightwave Technology, 36(7), 1390-1402.

[6] Al-Tarawneh, L., Alqatawneh, A., Tahat, A., & Saraereh, O. (2024). Evolution of optical networks: From legacy networks to next-generation networks. Journal of Optical Communications, 44(s1), s955-s970.

[7] Neffati, A. (2020). Automated classification and dataset creation based on YOLO neural network topologies (Doctoral dissertation, University of Sousse).

[8] Jemei, S., & Pahon, E. (2023). Encyclopedia of electrical and electronic power engineering. Control of fuel cell systems, 3, 472-484.

[9] Hui, R. (2019). Introduction to fiber-optic communications. Academic Press.

[10] Kareem, F. Q., Zeebaree, S. R., Dino, H. I., Sadeeq, M. A., Rashid, Z. N., Hasan, D. A., & Sharif, K. H. (2021). A survey of optical fiber communications: challenges and processing time influences. Asian Journal of Research in Computer Science, 7(4), 48-58.

[11] Keiser, G. (2021). Fiber optic communication networks. In Fiber Optic Communications (pp. 507-575). Singapore: Springer Singapore.

[12] Maulud, D. H., Zeebaree, S. R., Jacksi, K., Sadeeq, M. A. M., & Sharif, K. H. (2021). State of art for semantic analysis of natural language processing. Qubahan academic journal, 1(2), 21-28.

[13] Sadeeq, M. M., Abdulkareem, N. M., Zeebaree, S. R., Ahmed, D. M., Sami, A. S., & Zebari, R. R. (2021). IoT and Cloud computing issues, challenges and opportunities: A review. Qubahan Academic Journal, 1(2), 1-7.

[14] Misiakos, K., Kakabakos, S. E., Petrou, P. S., & Ruf, H. H. (2004). A monolithic silicon optoelectronic transducer as a real-time affinity biosensor. Analytical Chemistry, 76(5), 1366-1373.

[15] El-Hajj, M. (2025). Enhancing communication networks in the new era with artificial intelligence: techniques, applications, and future directions. Network, 5(1), 1.

[16] Kasneci, G., & Kasneci, E. (2024). Enriching tabular data with contextual LLM embeddings: A comprehensive ablation study for ensemble classifiers. arXiv preprint arXiv:2411.01645.

[17] Rashed, A. N. Z., Mahalakshmi, T., Sujaro, L., Ahammad, S. H., Hossain, M. A., & Mohammed, A. (2025). Visible-light wireless communication system performance signature improvement for high bit rate transmission system. Journal of Optical Communications, 45(s1), s1103-s1112.

[18] Binjumah, W. M. (2024). The Role of Machine Learning and Deep Learning Approaches to Improve Optical Communication Systems. Journal of Intelligent Learning Systems and Applications, 16(4), 418-429.

[19] Sherstinsky, A. (2020). Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Physica D: Nonlinear Phenomena, 404, 132306.

[20] Elmousalami, H. H. (2020). Artificial intelligence and parametric construction cost estimate modeling: State-of-the-art review. Journal of Construction Engineering and Management, 146(1), 03119008.

[21] Nagpal, A., & Gabrani, G. (2019, February). Python for data analytics, scientific and technical applications. In 2019 Amity international conference on artificial intelligence (AICAI) (pp. 140-145). IEEE.

@article{1710110,
      author = {Folakemi Judith Omogoroye, Guiawa Mathurine, Olubunmi Adewale Akinola, Onyegbadue Ikenna Augustine, Fred Izilein},
      title = {Analysis of the Performance for Quality of Transmission in Optical Fiber Communication Based on Machine Learning Optimization},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {2},
      pages = {873-879},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1710110.pdf},
      abstract = {The foundation and application of optical communication networks is the estimation of the optical signal?s Quality of Transmission (QoT) parameters from source to destination nodes. However, Machine Learning (ML) approaches are being used in recent research to increase the accuracy of QoT estimation. In this study, the performance validation was carried out through the application of ML computational technique and the results demonstrated a stable Bit Error Ratio (BER) of ~10??? and a consistent Signal-to-Noise Ratio (SNR) of 12.6?13.2 dB across transmission distances 19?22 km. In addition, the Phase-Shift Keying (PSK) and the Quadrature Phase-Shift Keying (QPSK) modulation outperformed the Amplitude Shift Keying (ASK) and Frequency Shift Keying (FSK), with statistical significance confirmed via chi-square tests. Conclusively, comparative advantages surpassed prior studies in BER reduction, SNR efficiency and multi-modulation adaptability.},
      keywords = {Artificial Intelligence; Communication; Optical fiber; Machine learning; Modelling; Transmission},
      month = {August},
  }