Document Type : Reseach Article
Authors
1 Department of Communication Engineering, College of Electronics Engineering, Ninevah University, Ninevah, Iraq.
2 Techniques Engineering Department, Technical Engineering College, Northern Technical University, Ninevah, Iraq.
Abstract
An efficient and remarkable automatic modulation classification (AMC) technique is essential with
the advent of sixth-generation (6G) communication systems. Using the pre-trained convolutional
neural network (CNN), a deep learning (DL) approach to classify eight types of digital modulated
signals. National Instrument LabVIEW NXG is used to build the modulation transceivers at
100 GHz, a 6G carrier frequency. The dataset was collected in a complicated environment, including carrier frequency offset (CFO), phase noise (PN), and distinct signal-to-noise ratios (SNR). Through experimental simulation, an improvement in the classification accuracies was achieved. In particular, the outstanding accuracy rates achieved are 98.68% and 96.05% using ResNet18 and ResNet101, respectively. Furthermore, these models can classify the modulated
signals at lower SNRs. These innovative models are suitable and effective to utilize for 6G wireless communication networks.
Keywords
- Kumar, Sunil, and Kavita Rathore. "Renewable Energy for Sustainable Development Goal of Clean and Affordable Energy." International Journal of Materials Manufacturing and Sustainable Technologies, 2 (1), 1–15. DOI: https://doi.org/10.56896/IJMMST.2023.2.1.001
- Yekinni, Sanusi, et al. “Solar Photovoltaic Energy System’’. Nanogenerators and Self-Powered Systems, IntechOpen, 24 May 2023. DOI: 10.5772/intechopen.108958
- Kashani, Marziyeh, et al. "The Optimization of Photovoltaic Systems Design Using Mathematical Modeling and QFD-DSM Methods." Majlesi Journal of Electrical Engineering 16.2 (2022): 55-72. https://doi.org/10.30486/mjee.2022.696495
- Seda.gov.my. (2020). FiT – Renewable Energy Malaysia. [online].
- Obaideen, Khaled, et al. "Solar energy: Applications, trends analysis, bibliometric analysis and research contribution to sustainable development goals (SDGs)." Sustainability 15.2 (2023): 1418. https://doi.org/10.3390/su15021418
- Putri, Raihan, et al. "Design and Implementation of a Solar Power System on Grid SDN 023905 BIN JAI using PVSYST Software." Majlesi Journal of Electrical Engineering 17.3 (2023). https://doi.org/10.30486/mjee.2023.1989935.1167
- Setyawati, Harsasi, et al. "Photovoltaic Performance of Naphthol Blue Black Complexes and their Band Gap Energy." International Journal of Nanoelectronics and Materials 16.2 (2023): 335-344 https://doi.org/10.58915/ijneam.v16i2.1232
- Ameur, Arechkik, et al. "Forecast modeling and performance assessment of solar PV systems." Journal of Cleaner Production 267 (2020): 122167. https://doi.org/10.1016/j.jclepro.2020.122167
- Mishra, Vijay Laxmi, Yogesh K. Chauhan, and Kripa S. Verma. "Various modeling approaches of photovoltaic module: A comparative analysis." Majlesi Journal of Electrical Engineering 17.2 (2023): 117-131. 10.30486/mjee.2023.1984023.1109
- Nawab, Faisal, et al. "Solar irradiation prediction using empirical and artificial intelligence methods: A comparative review." Heliyon (2023). https://doi.org/10.1016/j.heliyon.2023.e17038
- Jamil, Wan Juzaili, et al. "Modeling of soiling derating factor in determining photovoltaic outputs." IEEE Journal of Photovoltaics 10.5 (2020): 1417-1423. https://doi.org/10.1109/JPHOTOV.2020.3003815
- Liu, Shicheng, Ruidong Chang, Jian Zuo, Ronald J. Webber, Feng Xiong, and Na Dong. "Application of artificial neural networks in construction management: Current status and future directions." Applied Sciences 11, no. 20 (2021): 9616. https://doi.org/10.3390/app11209616
- Mahadzir, Cempaka Amalin, Ahmad Fateh Mohamad Nor, and Siti Amely Jumaat. "Photovoltaic Power Output Prediction using Graphical User Interface and Artificial Neural Network." Majlesi Journal of Electrical Engineering 17.4 (2023): 73-78. https://doi.org/10.30486/mjee.2023.1995210.1243
- Nawab, Faisal, et al. "Solar irradiation prediction using empirical and artificial intelligence methods: A comparative review." Heliyon (2023). https://doi.org/10.1016/j.heliyon.2023.e17038
- Sustainable Energy Development Authority (SEDA MALAYSIA), “Fundamentals of Solar Photovoltaics Technology”. Chapter 5: Photovoltaic Technology, page 77. (2022). (ISBN 978-967-12390-7-0).
- Franklin, E. A. (2019). “Calculations for a Grid-Connected Solar Energy System.” University of Arizona Cooperative Extension: Tucson, AZ, USA, 2-6.
- Sara, Umme, Morium Akter, and Mohammad Shorif Uddin. "Image quality assessment through FSIM, SSIM, MSE and PSNR—a comparative study." Journal of Computer and Communications 7.3 (2019): 8-18. https://doi.org/10.4236/jcc.2019.73002
- Mayer, M. J., & Gróf, G. (2021). “Extensive comparison of physical models for photovoltaic power forecasting.” Applied Energy, 283, 116239. https://doi.org/10.1016/j.apenergy.2020.116239
)