Signal Modulation Recognition System Based on Different Signal Noise Rate Using Artificial Intelligent Approach
Everyone has paid much attention to modulation-type recognition in the past few years. There are many ways to find the modulation type, but only a few good ways to deal with signals with a lot of noise. This study comes up with a way to test how well different machine learning algorithms can handle...
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| Format: | Article |
| Language: | English |
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Engiscience Publisher
2022-12-01
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| Series: | Journal of Studies in Science and Engineering |
| Subjects: | |
| Online Access: | https://engiscience.com/index.php/josse/article/view/45 |
| _version_ | 1797836162564358144 |
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| author | Rasool F. Jader Mudhafar Haji M. Abd Ihsan Hamza Jumaa |
| author_facet | Rasool F. Jader Mudhafar Haji M. Abd Ihsan Hamza Jumaa |
| author_sort | Rasool F. Jader |
| collection | DOAJ |
| description | Everyone has paid much attention to modulation-type recognition in the past few years. There are many ways to find the modulation type, but only a few good ways to deal with signals with a lot of noise. This study comes up with a way to test how well different machine learning algorithms can handle noise when detecting digital and analogue modulations. This study looks at the four most common digital and analogue modulations: Phase Shift Keying, Quadrature Phase Shift Keying, Amplitude Modulation, and Morse Code. A signal noise rate from -10dB to +25dB is used to find these modulations. We used machine learning algorithms to determine the modulation type like Decision Tree, Random Forest, Support Vectors Machine, and k-nearest neighbours. After the IQ samples had been converted to the amplitude of samples and radio frequency format, the accuracy of each method looked good. Still, in the format of the sample phase, each algorithm's accuracy was less. The results show that the proposed method works to find the signals that have noises. When there is less noise, the random forest algorithm gives better results than SVM, but SVM gives better results when there is more noise. |
| first_indexed | 2024-04-09T15:04:13Z |
| format | Article |
| id | doaj.art-9a54b0a5551746109fcbd97db7c4aa2e |
| institution | Directory Open Access Journal |
| issn | 2789-634X |
| language | English |
| last_indexed | 2024-04-09T15:04:13Z |
| publishDate | 2022-12-01 |
| publisher | Engiscience Publisher |
| record_format | Article |
| series | Journal of Studies in Science and Engineering |
| spelling | doaj.art-9a54b0a5551746109fcbd97db7c4aa2e2023-04-30T22:09:46ZengEngiscience PublisherJournal of Studies in Science and Engineering2789-634X2022-12-0124374910.53898/josse202224445Signal Modulation Recognition System Based on Different Signal Noise Rate Using Artificial Intelligent ApproachRasool F. Jader0https://orcid.org/0000-0003-4563-4050Mudhafar Haji M. Abd1https://orcid.org/0000-0001-6858-1268Ihsan Hamza Jumaa2https://orcid.org/0000-0003-1453-7020Computer Science Department, Faculty of Science, Soran University, Soran, 44008, IraqComputer Science Department, Faculty of Science, Soran University, Soran, 44008, IraqComputer Department, Rwandiz Institute, Soran, 44008, IraqEveryone has paid much attention to modulation-type recognition in the past few years. There are many ways to find the modulation type, but only a few good ways to deal with signals with a lot of noise. This study comes up with a way to test how well different machine learning algorithms can handle noise when detecting digital and analogue modulations. This study looks at the four most common digital and analogue modulations: Phase Shift Keying, Quadrature Phase Shift Keying, Amplitude Modulation, and Morse Code. A signal noise rate from -10dB to +25dB is used to find these modulations. We used machine learning algorithms to determine the modulation type like Decision Tree, Random Forest, Support Vectors Machine, and k-nearest neighbours. After the IQ samples had been converted to the amplitude of samples and radio frequency format, the accuracy of each method looked good. Still, in the format of the sample phase, each algorithm's accuracy was less. The results show that the proposed method works to find the signals that have noises. When there is less noise, the random forest algorithm gives better results than SVM, but SVM gives better results when there is more noise.https://engiscience.com/index.php/josse/article/view/45machine learningammorse codepskqpsk |
| spellingShingle | Rasool F. Jader Mudhafar Haji M. Abd Ihsan Hamza Jumaa Signal Modulation Recognition System Based on Different Signal Noise Rate Using Artificial Intelligent Approach Journal of Studies in Science and Engineering machine learning am morse code psk qpsk |
| title | Signal Modulation Recognition System Based on Different Signal Noise Rate Using Artificial Intelligent Approach |
| title_full | Signal Modulation Recognition System Based on Different Signal Noise Rate Using Artificial Intelligent Approach |
| title_fullStr | Signal Modulation Recognition System Based on Different Signal Noise Rate Using Artificial Intelligent Approach |
| title_full_unstemmed | Signal Modulation Recognition System Based on Different Signal Noise Rate Using Artificial Intelligent Approach |
| title_short | Signal Modulation Recognition System Based on Different Signal Noise Rate Using Artificial Intelligent Approach |
| title_sort | signal modulation recognition system based on different signal noise rate using artificial intelligent approach |
| topic | machine learning am morse code psk qpsk |
| url | https://engiscience.com/index.php/josse/article/view/45 |
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