JCSE, vol. 16, no. 1, pp.25-42, 2022

DOI: http://dx.doi.org/10.5626/JCSE.2022.16.1.25

Automatic Modulation Recognition Using Minimum-Phase Reconstruction Coefficients and Feed-Forward Neural Network

Sunday Ajala, Emmanuel Adetiba, Oluwaseun T. Ajayi, Abdultaofeek Abayomi, Anabi Hilary Kelechi, Joke A. Badejo, Sibusiso Moyo, and Murimo Bethel Mutanga
Department of Engineering, Norfolk State University, Norfolk, VA, USA Department of Electrical & Information Engineering, Covenant Applied Informatics & Communication Africa Center of Excellence (CApIC-ACE), Covenant University, Ota, Nigeria Institute for Systems Science, HRA, Durban University of Technology, Durban, South Africa Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL, USA Department of Information and Communication Technology, Mangosuthu University of Technology, Umlazi, Durban, South Africa Department of Electrical & Information Engineering, Covenant Applied Informatics & Communication Africa Center of Excellence (CApIC-ACE), Covenant University, Ota, Nigeria Office of DVC Research, Innovation & Engagement, Institute for Systems Science, Durban University of Technology, Durban, South Africa Department of Information and Communication Technology, Mangosuthu University of Technology, Umlazi, Durban, South Africa

Abstract: Identification of signal waveforms is highly critical in 5G communications and other state-of-the-art radio technologies such as cognitive radios. For instance, to achieve efficient demodulation and spectrum sensing, cognitive radios need to implement automatic modulation recognition (AMR) of detected signals. Although many works have been reported in the literature on the subject, most of them have mainly focused on the additive white Gaussian noise (AWGN) channel. However, addressing the AWGN channel, only, does not sufficiently emulate real-time wireless communications. In this paper, we created datasets of six modulation schemes in GNU Radio. Wireless signal impairment issues such as center frequency offset, sample rate offset, AWGN, and multipath fading effects were applied for the dataset creation. Afterward, we developed AMR models by training different artificial neural network (ANN) architectures using real cepstrum coefficients (RCC), and minimum-phase reconstruction coefficients (MPRC) extracted from the created signals. Between these two features, MPRC features have the best performance, and the ANN architecture with Levenberg-Marquardt learning algorithm, as well as logsig and purelin activation functions in the hidden and output layers, respectively, gave the best performance of 98.7% accuracy, 100% sensitivity, and 99.33% specificity when compared with other algorithms. This model can be leveraged in cognitive radio for spectrum sensing and automatic selection of signal demodulators.

Keyword: Cognitive radio; Cepstrum analysis; GNU Radio; Modulation schemes; MPRC; RCC

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