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Jagannath, A. and Jagannath, J. (2021) Dataset for Modulation Classification and Signal Type Classification for Multi-Task and Single Task Learning. Computer Networks, 199, Article ID: 108441.
https://doi.org/10.1016/j.comnet.2021.108441

has been cited by the following article:

• TITLE: Investigation on Analog and Digital Modulations Recognition Using Machine Learning Algorithms

  AUTHORS: Jean Ndoumbe, Ivan Basile Kabeina, Gaelle Patricia Talotsing, Soubiel-Noël Nkomo Biloo

  KEYWORDS: Automatic Recognition, Artificial Neural Networks, K-Nearest Neighbors, Machine Learning, Analog Modulations, Digital Modulations

  JOURNAL NAME: World Journal of Engineering and Technology, Vol.12 No.4, September 30, 2024

  ABSTRACT: In the field of radiocommunication, modulation type identification is one of the most important characteristics in signal processing. This study aims to implement a modulation recognition system on two approaches to machine learning techniques, the K-Nearest Neighbors (KNN) and Artificial Neural Networks (ANN). From a statistical and spectral analysis of signals, nine key differentiation features are extracted and used as input vectors for each trained model. The feature extraction is performed by using the Hilbert transform, the forward and inverse Fourier transforms. The experiments with the AMC Master dataset classify ten (10) types of analog and digital modulations. AM_DSB_FC, AM_DSB_SC, AM_USB, AM_LSB, FM, MPSK, 2PSK, MASK, 2ASK, MQAM are put forward in this article. For the simulation of the chosen model, signals are polluted by the Additive White Gaussian Noise (AWGN). The simulation results show that the best identification rate is the MLP neuronal method with 90.5% of accuracy after 10 dB signal-to-noise ratio value, with a shift of more than 15% from the k-nearest neighbors’ algorithm.