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Modulation Format Identification Using Supervised Learning and High-Dimensional Features

  • Research Article-Computer Engineering and Computer Science
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Abstract

The popular directions for automatic modulation classification algorithms investigate how to develop feature extraction methods for further signal classification. In this paper, we propose a mapping algorithm for a manually designed feature extraction method by using the reconstruction component of principal component analysis (PCA), which further extracts discrimination between signal features via a PCA reconstruction component. Two supervised neural network models are studied to achieve the limits of the learning matrix in modulation signal classification. Some experimental results show that different modulation schemes can be obviously classified using matrix mapping for feature extraction. Moreover, the modulation classification accuracy based on the mapping extraction feature, which has a lower SNR requirement for training, is slightly improved compared with some triradial deep learning methods.

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Gaziantep University, 27310, Gaziantep, Turkey

    Ahmed K. Ali & Ergun Erçelebi

  2. Al-Mustansiriyah University, Baghdad, Iraq

    Ahmed K. Ali

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  1. Ahmed K. Ali
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  2. Ergun Erçelebi
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Correspondence to Ahmed K. Ali.

Appendix A

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Table 12 Parameters of the multipath fading channel models
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Ali, A.K., Erçelebi, E. Modulation Format Identification Using Supervised Learning and High-Dimensional Features. Arab J Sci Eng 48, 1461–1486 (2023). https://doi.org/10.1007/s13369-022-06887-2

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  • DOI: https://doi.org/10.1007/s13369-022-06887-2

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