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Machine learning based 64-QAM classification techniques for enhanced optical communication

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Abstract

Due to their greatly increased spectrum efficiency, high-order quadrature amplitude modulation (QAM) formats are especially successful at increasing transmission capacity. QAM is extremely sensitive to nonlinear distortion because of its dense constellation and SNR-hungry configuration. Autonomous neural network (ANN) derived nonlinear decision boundaries that are adaptively created by machine learning techniques can be used to classify symbols. The proposed work focusing on the quadrature amplitude modulation (QAM) scheme, the approach is to formulate an autonomous neural network (ANN) that can predict the class of each symbol from a signal stream of symbols. Experimental accuracy for each ANN's of proposed work achieves 89% by analysing all tests. Comprehensive results are presented with comparisons, demonstrating notable nonlinear mitigation with BER reductions. Additionally, it offers a glimpse into potential future research plans intended to raise the likelihood that predictions would come true and their accuracy.

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This research received no external funding.

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Authors and Affiliations

  1. Department of ECE, Vidyavardhaka College of Engineering, Mysuru, India

    P. Kiran

  2. Department of Information Technology, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India

    H. L. Gururaj

  3. School of Innovation, Design and Engineering, Malardalen University, Vasteras, Sweden

    Francesco Flammini

  4. Department of MCA, Administrative Management College, Bengaluru, India

    D. S. Sunil Kumar

  5. Department of ECE, Alva’s Institute of Engineering and Technology, Moodbidri, India

    V. Veeraprathap

Authors
  1. P. Kiran
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  2. H. L. Gururaj
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  3. Francesco Flammini
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  4. D. S. Sunil Kumar
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  5. V. Veeraprathap
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Contributions

For research articles with several authors, a short paragraph specifying their individual contributions must be provided. The following statements should be used “Conceptualization, methodology done by KP and SDS, software, validation by Veeraprathap, formal analysis, investigation done by GHL, writing—review and editing, funding acquisition by FF. All authors have read and agreed to the published version of the manuscript.”

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Correspondence to H. L. Gururaj.

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Kiran, P., Gururaj, H.L., Flammini, F. et al. Machine learning based 64-QAM classification techniques for enhanced optical communication. Opt Quant Electron 55, 1179 (2023). https://doi.org/10.1007/s11082-023-05472-7

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