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Automatic modulation classification in optical wireless communication systems based on cancellable biometric concepts

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Abstract

Automatic modulation classification (AMC) has recently acquired a lot of interest in the optical wireless communication (OWC) community. The OWC channel has variable characteristics. Hence, there is a need for an adaptive modulation scheme to cope with the varying channel characteristics. Adaptive modulation requires the implementation of AMC at the receiver side. Instead of using complex classification with deep learning (DL) techniques, a simple proposed scheme for AMC is introduced in this paper. This scheme is based on chaotic Baker map (CBM), wavelet image fusion, and autocorrelation estimation. It depends on constellation diagrams for eight modulation formats, including (2/4/8/16 PSK), and (8/16/32/64 QAM). First, the constellation diagrams are acquired and scrambled through the CBM, and they are merged using the wavelet image fusion and stored as reference templates in the system database. After that, the classification of each modulation format depends on estimated correlation scores and a thresholding strategy similar to the strategy adopted in cancellable biometric systems that depend on encrypted templates. Simulation results prove good classification for all studied modulation formats.

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Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

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Authors and Affiliations

  1. Department of Electrical Engineering, Faculty of Engineering, Minia University, Minia, 61111, Egypt

    Safie El-Din Nasr Mohamed & Ashraf A. M. Khalaf

  2. Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt

    Bidaa Mortada, Walid El-Shafai, O. Zahran, Moawad I. Dessouky, El-Sayed M. El-Rabaie & Fathi E. Abd El-Samie

  3. Security Engineering Lab, Computer Science Department, Prince Sultan University, Riyadh, 11586, Saudi Arabia

    Walid El-Shafai

  4. Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdurrahman University, Riyadh, 21974, Saudi Arabia

    Fathi E. Abd El-Samie

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  1. Safie El-Din Nasr Mohamed
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  2. Bidaa Mortada
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  3. Walid El-Shafai
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  4. Ashraf A. M. Khalaf
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  5. O. Zahran
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  6. Moawad I. Dessouky
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  8. Fathi E. Abd El-Samie
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Correspondence to Safie El-Din Nasr Mohamed.

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Mohamed, S.ED.N., Mortada, B., El-Shafai, W. et al. Automatic modulation classification in optical wireless communication systems based on cancellable biometric concepts. Opt Quant Electron 55, 389 (2023). https://doi.org/10.1007/s11082-022-04446-5

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