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

Ajala, Sunday and Adetiba, E. and Ajayi, Oluwaseun T. and Abayomi, Abdultaofeek and Anabi, Hilary Kelechi and Badejo, J. A. and Moyo, Sibusiso and Mutanga, Murimo Bethel (2022) Automatic Modulation Recognition Using Minimum-Phase Reconstruction Coefficients and Feed-Forward Neural Network. Journal of Computer Science, 16 (1). pp. 25-42. ISSN 1976-4677

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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.

Item Type:	Article
Uncontrolled Keywords:	Cognitive radio; Cepstrum analysis; GNU Radio; Modulation scheme
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:	Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
Depositing User:	nwokealisi
Date Deposited:	18 Jan 2023 08:45
Last Modified:	18 Jan 2023 08:45
URI:	http://eprints.covenantuniversity.edu.ng/id/eprint/16530

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