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Automatic Modulation Classification Based on Machine Learning

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Advances in Automation, Mechanical and Design Engineering (SAMDE 2021)

Part of the book series: Mechanisms and Machine Science ((Mechan. Machine Science,volume 121))

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Abstract

Automatic Modulation Classification (AMC) is a rapidly evolving technology, which can be employed in software defined radio structures, such as for military communication. Machine Learning can provide novel and efficient technology for modulation classification, especially for systems working in low Signal to Noise Ratio (SNR). For this work, a dynamic modulation classification system without phase lock is trialed. The signals are captured with different SNR and duration. Traditional Machine Learning based on the mathematical features is compared with Deep Learning based on the constellations. Based on these two methods, a hybrid model is provided. This model involved the novel Deep Learning at first and the feature classification as a supplement, which achieves good performance at low SNR area.

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Authors and Affiliations

  1. The University of Sheffield, Sheffiels, United Kingdom

    Yilin Sun & Eddie Ball

Authors
  1. Yilin Sun
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  2. Eddie Ball
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Corresponding author

Correspondence to Yilin Sun .

Editor information

Editors and Affiliations

  1. SP2MI—Site du Futuroscope, University of Poitiers, Poitiers, France

    Med Amine Laribi

  2. DIMEG, University of Calabria, Rende, Italy

    Giuseppe Carbone

  3. iOPEN, Northwestern Polytechnical University, Xi’an, Shaanxi, China

    Zhiyu Jiang

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Sun, Y., Ball, E. (2023). Automatic Modulation Classification Based on Machine Learning. In: Laribi, M.A., Carbone, G., Jiang, Z. (eds) Advances in Automation, Mechanical and Design Engineering. SAMDE 2021. Mechanisms and Machine Science, vol 121. Springer, Cham. https://doi.org/10.1007/978-3-031-09909-0_5

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