Adetiba, E. and Olaloye, Folarin Joseph and Abayomi, Abdultaofeek and Faruk, Nasir and Moyo, Sibusiso and Obiyemi, O. O. and Thakur, Surendra (2022) Compact automatic modulation recognition using over-the-air signals and FOS features. Bulletin of Electrical Engineering and Informatics, 11 (4). pp. 2013-2024. ISSN 2302-9285
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Abstract
The recent deployment of automatic modulation recognition (AMR) for cognitive radio (CR) systems has significantly enhanced spectrum sensing capabilities. The utilization of real-time over-the-air digital radio frequency (RF) data for the development of a digital spectrum sensing model based on the automatic modulation classification (AMC) is presented in this study as a step for incorporating opportunistic spectrum sensing onto the NomadicBTS architecture. Some digital modulation techniques were studied for second- generation (2G) through fourth-generation (4G) technology. The raw RF signal dataset was digitized and curated, while non-complex first-order statistical (FOS) features were used with algorithms based on the Scaled conjugate gradient (SCG) and Levenberg-Marquardt (LM) to find the best learning algorithm for the generated AMR model. The results show that the developed AMR model has a very high likelihood of correctly classifying signals, with distinct patterns for each of the features of FOS. The results are compared to reveal a least mean square error (MSE) of 0.0131 with a maximum accuracy of 93.5 percent when the model was trained with seventy (70) neurons in the hidden layer using the LM method. The best model's accuracy will allow for the most precise identification of spectrum holes in the bands under consideration
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Automatic modulation recognition Cognitive radio NomadicBTS Software defined radio Spectrum sensing |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science |
| Depositing User: | AKINWUMI |
| Date Deposited: | 21 Nov 2022 15:28 |
| Last Modified: | 21 Nov 2022 15:28 |
| URI: | http://eprints.covenantuniversity.edu.ng/id/eprint/16464 |
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