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CSA-Assisted Gabor Features for Automatic Modulation Classification

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Abstract

Automatic modulation classification (AMC) is a process of automatic detection of modulation format imposed on the received signal with no prior information (carrier, signal power, phase offset) of the signal, also known as blind classification. In this paper, we proposed a new AMC algorithm, by combining the synergy of the meta-heuristic technique with Gabor feature extraction mainly used in texture analysis. Gabor filters are used to extract the features that are further optimized using the cuckoo search algorithm to increase the efficiency of the classification procedure. The classification approach is applied on digitally modulated signals having phase-shift keying, frequency-shift keying, and quadrature amplitude modulation schemes of order 2–64 over the nonfading channel (AWGN) and fading channel (Rayleigh). Simulations and performance comparison with the existing literature validate that the proposed solution has better classification accuracy with lower sample size and lower signal-to-noise ratio.

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Data Availability

Data sharing does not apply to this article as random signals were generated or analyzed during the current study.

Code Availability

MATLAB code for the current study will be made available at reasonable request.

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Author notes

  1. Antonio Coronato, Sajjad A. Ghauri, Sheraz Alam, and Mubashar Sarfraz have contributed equally to this work.

Authors and Affiliations

  1. Institute of High Performance Computing and Networking (ICAR), National Research Council of Italy, Napoli, Italy

    Syed Ihtesham Hussain Shah & Antonio Coronato

  2. Department of Electrical Engineering, ISRA University, Islamabad Campus, Islamabad, Pakistan

    Sajjad A. Ghauri

  3. Department of Electrical Engineering, NUML University, Islamabad, Pakistan

    Sheraz Alam & Mubashar Sarfraz

Authors
  1. Syed Ihtesham Hussain Shah
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  2. Antonio Coronato
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  3. Sajjad A. Ghauri
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  4. Sheraz Alam
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  5. Mubashar Sarfraz
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Correspondence to Syed Ihtesham Hussain Shah.

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Shah, S.I.H., Coronato, A., Ghauri, S.A. et al. CSA-Assisted Gabor Features for Automatic Modulation Classification. Circuits Syst Signal Process 41, 1660–1682 (2022). https://doi.org/10.1007/s00034-021-01854-y

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  • DOI: https://doi.org/10.1007/s00034-021-01854-y

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