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Energy efficient optimization using RTSO machine learning approach towards next generation optical network circuit for smart cities

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Abstract

Modern telecommunications cannot function without optical communication networks, which enable high-speed data transmission across large distances. However, several variables, such as peak-to-average power ratio (PAPR) distortion, might impact the signal's quality. In optical communication networks, PAPR deformation is a significant problem that may contribute to signal deterioration and deformation, resulting in errors in the delivered data. To lower the PAPR of the message in DC-biased optical communication networks, this research attempts to design an efficient and effective optimization methodology for smart cities. A robust tree-seed optimization (RTSO) algorithm is suggested explicitly in this study as a brand-new optimization method to deal with this issue. According to the convergence assessment, the RTSO technique converges more quickly than other optimization techniques. In conclusion, the suggested RTSO algorithm offers a practical and efficient solution to the PAPR issue in DC-biased optical communications networks. The method can enhance optical communication efficiency and lessen PAPR's detrimental effects on signal quality which can be used in smart cities.Please confirm if the author names are presented accurately and in the correct sequence (given name, middle name/initial, family name). Author 1 Given name: [Md Altab] Last name [Hossin], Author 2 Given name: [Jamal Ahmed] Last name [Alenizi]. Also, kindly confirm the details in the metadata are correct.Thank you. Yes, It is right.Author details: Kindly check and confirm whether the corresponding affiliation is correctly identified.Yes, It is right

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Acknowledgements

This work was funded by the Deanship of Scientific Research at Jouf University under Grant Number (DSR2022-RG-0103)

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The authors have not disclosed any funding.

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Authors and Affiliations

  1. Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakakah, Saudi Arabia

    Saad Alanazi, M. M. Kamruzzaman, Nasser Alshammari, Ibrahim Alrashdi & Jamal Ahmed Alenizi

  2. School of Innovation and Entrepreneurship, Chengdu University, Chengdu, Sichuan, People’s Republic of China

    Md Altab Hossin

  3. Department of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, Sakakah, Saudi Arabia

    Madallah Alruwaili

Authors
  1. Saad Alanazi
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  2. M. M. Kamruzzaman
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  3. Md Altab Hossin
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  4. Madallah Alruwaili
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  5. Nasser Alshammari
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  6. Ibrahim Alrashdi
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  7. Jamal Ahmed Alenizi
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Contributions

Conceptualization, SA, MMK and MdAH; Data curation, SA, MMK, MA, NA, IA and JAA; Methodology, MA and IA, JAA; Resources, SA and MdAH; Writing – original draft, MMK, SA, MdAH, MA, NA; Writing – Review & Editing, MMK, SA, MdAH, MA, NA, IA and JAA.

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Correspondence to M. M. Kamruzzaman or Md Altab Hossin.

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Alanazi, S., Kamruzzaman, M.M., Hossin, M.A. et al. Energy efficient optimization using RTSO machine learning approach towards next generation optical network circuit for smart cities. Opt Quant Electron 56, 36 (2024). https://doi.org/10.1007/s11082-023-05600-3

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