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Automatic Modulation Recognition of Analog Modulation Signals Using Convolutional Neural Network

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Evolution in Signal Processing and Telecommunication Networks

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 839))

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Abstract

In this study, different deep learning approaches are applied to automatic modulation classifier (AMC). AMC can change its characteristics based on channel conditions. It gained importance in crowded spectrum due to its numerous advantages. The primary goal of the study is to guide researchers to choose appropriate technique based on channel conditions and modulation classes from pool of available deep learning (DL) techniques. In this paper, simulations are carried on analog signals using CNN. Furthermore, merits and demerits of proposed approach are discussed.

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Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, JNTU Kakinada, Kakinada, Andhra Pradesh, India

    N. Venkateswara Rao & B. T. Krishna

Authors
  1. N. Venkateswara Rao
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  2. B. T. Krishna
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Editor information

Editors and Affiliations

  1. Dept of ECE, Raghu Institute of Technology, Visakhapatnam, Andhra Pradesh, India

    P. Satish Rama Chowdary

  2. Department of Electronics and Telecommunication, Ramon Llull University, Barcelona, Barcelona, Spain

    Jaume Anguera

  3. School of Computer Engineering, Kalinga Institute Industrial Tech Univ, Bhubaneswar, Odisha, India

    Suresh Chandra Satapathy

  4. Dept of Electronics & Comm Engg, Shri Ramswaroop Memorial Group of Professional Colleges (SRMGPC), Lucknow, Uttar Pradesh, India

    Vikrant Bhateja

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Rao, N.V., Krishna, B.T. (2022). Automatic Modulation Recognition of Analog Modulation Signals Using Convolutional Neural Network. In: Chowdary, P.S.R., Anguera, J., Satapathy, S.C., Bhateja, V. (eds) Evolution in Signal Processing and Telecommunication Networks. Lecture Notes in Electrical Engineering, vol 839. Springer, Singapore. https://doi.org/10.1007/978-981-16-8554-5_39

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  • DOI: https://doi.org/10.1007/978-981-16-8554-5_39

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-8553-8

  • Online ISBN: 978-981-16-8554-5

  • eBook Packages: EngineeringEngineering (R0)

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