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MATLAB-simulated dataset for automatic modulation classification in wireless fading channels.

M M Sadman Shafi1, Tasnia Siddiqua Ahona1, Ashraful Islam Mridha1

  • 1Department of Electrical and Electronic Engineering, Islamic University of Technology, Board Bazar, Gazipur 1704, Bangladesh.

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This study introduces a synthetic dataset for wireless modulation classification, crucial for cognitive radio and adaptive communications. It features diverse modulation schemes and realistic channel conditions, aiding machine learning model development.

Keywords:
Adaptive communicationFeature extractionImage processingMachine LearningSignal processingSpectrogram

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Area of Science:

  • Wireless Communications
  • Signal Processing
  • Machine Learning

Background:

  • Accurate modulation classification is vital for cognitive radio and adaptive communication systems.
  • Dynamic channels and lack of transmitter knowledge pose significant challenges.

Purpose of the Study:

  • To present a labeled synthetic dataset for wireless modulation classification.
  • To facilitate the development and evaluation of machine learning models under realistic propagation scenarios.

Main Methods:

  • Generated synthetic signals using five digital modulation schemes (BPSK, QPSK, 16-QAM, 64-QAM, 256-QAM).
  • Simulated signals through Rayleigh and Rician fading channels with added impairments.
  • Extracted diverse features including statistical, time-domain, frequency-domain, spectrogram-based, spectral correlation-based, and image processing-based descriptors.
  • Organized the dataset into CSV files across various sampling frequencies (1 MHz to 1 GHz) and channel types.

Main Results:

  • A comprehensive dataset with labeled synthetic signals under realistic wireless conditions was created.
  • Included MATLAB scripts for signal generation and feature extraction to ensure reproducibility.
  • The dataset covers multiple modulation types, channel models, and sampling frequencies.

Conclusions:

  • The presented dataset serves as a valuable benchmark for advancing modulation classification research.
  • It supports the development and evaluation of machine learning algorithms for signal identification in wireless communications.