Automatic Classification of 5G Waveform-Modulated Signals Using Deep Residual Networks
Modulation identification plays a crucial role in contemporary wireless communication systems, especially within 5G and future-generation networks that utilize a variety of multicarrier waveforms. This study introduces an innovative algorithm for automatic modulation classification (AMC) built on a...
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MDPI AG
2025-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/15/4682 |
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| author | Haithem Ben Chikha Alaa Alaerjan Randa Jabeur |
| author_facet | Haithem Ben Chikha Alaa Alaerjan Randa Jabeur |
| author_sort | Haithem Ben Chikha |
| collection | DOAJ |
| description | Modulation identification plays a crucial role in contemporary wireless communication systems, especially within 5G and future-generation networks that utilize a variety of multicarrier waveforms. This study introduces an innovative algorithm for automatic modulation classification (AMC) built on a deep residual network (DRN) architecture. The approach is tailored to accurately identify advanced 5G waveform types such as Orthogonal Frequency-Division Multiplexing (OFDM), Filtered OFDM (FOFDM), Filter Bank Multicarrier (FBMC), Universal Filtered Multicarrier (UFMC), and Weighted Overlap and Add OFDM (WOLA), using both 16-QAM and 64-QAM modulation schemes. To our knowledge, this is the first application of deep learning in the classification of such a diverse set of complex 5G waveforms. The proposed model combines the deep learning capabilities of DRNs for feature extraction with Principal Component Analysis (PCA) for dimensionality reduction and feature refinement. A detailed performance evaluation is conducted using metrics like classification recall, precision, accuracy, and F-measure. When compared with traditional machine learning approaches reported in recent studies, our DRN-based method shows significantly improved classification accuracy and robustness. These results highlight the effectiveness of deep residual networks in improving adaptive signal processing and enabling automatic modulation recognition in future wireless communication technologies. |
| format | Article |
| id | doaj-art-ffdebc6d6f6a4ad6be4b011d0af87f14 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-ffdebc6d6f6a4ad6be4b011d0af87f142025-08-20T03:36:30ZengMDPI AGSensors1424-82202025-07-012515468210.3390/s25154682Automatic Classification of 5G Waveform-Modulated Signals Using Deep Residual NetworksHaithem Ben Chikha0Alaa Alaerjan1Randa Jabeur2Department of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, Sakaka 72341, Saudi ArabiaDepartment of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72341, Saudi ArabiaDepartment of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72341, Saudi ArabiaModulation identification plays a crucial role in contemporary wireless communication systems, especially within 5G and future-generation networks that utilize a variety of multicarrier waveforms. This study introduces an innovative algorithm for automatic modulation classification (AMC) built on a deep residual network (DRN) architecture. The approach is tailored to accurately identify advanced 5G waveform types such as Orthogonal Frequency-Division Multiplexing (OFDM), Filtered OFDM (FOFDM), Filter Bank Multicarrier (FBMC), Universal Filtered Multicarrier (UFMC), and Weighted Overlap and Add OFDM (WOLA), using both 16-QAM and 64-QAM modulation schemes. To our knowledge, this is the first application of deep learning in the classification of such a diverse set of complex 5G waveforms. The proposed model combines the deep learning capabilities of DRNs for feature extraction with Principal Component Analysis (PCA) for dimensionality reduction and feature refinement. A detailed performance evaluation is conducted using metrics like classification recall, precision, accuracy, and F-measure. When compared with traditional machine learning approaches reported in recent studies, our DRN-based method shows significantly improved classification accuracy and robustness. These results highlight the effectiveness of deep residual networks in improving adaptive signal processing and enabling automatic modulation recognition in future wireless communication technologies.https://www.mdpi.com/1424-8220/25/15/4682modulation classificationdeep residual networks5G |
| spellingShingle | Haithem Ben Chikha Alaa Alaerjan Randa Jabeur Automatic Classification of 5G Waveform-Modulated Signals Using Deep Residual Networks Sensors modulation classification deep residual networks 5G |
| title | Automatic Classification of 5G Waveform-Modulated Signals Using Deep Residual Networks |
| title_full | Automatic Classification of 5G Waveform-Modulated Signals Using Deep Residual Networks |
| title_fullStr | Automatic Classification of 5G Waveform-Modulated Signals Using Deep Residual Networks |
| title_full_unstemmed | Automatic Classification of 5G Waveform-Modulated Signals Using Deep Residual Networks |
| title_short | Automatic Classification of 5G Waveform-Modulated Signals Using Deep Residual Networks |
| title_sort | automatic classification of 5g waveform modulated signals using deep residual networks |
| topic | modulation classification deep residual networks 5G |
| url | https://www.mdpi.com/1424-8220/25/15/4682 |
| work_keys_str_mv | AT haithembenchikha automaticclassificationof5gwaveformmodulatedsignalsusingdeepresidualnetworks AT alaaalaerjan automaticclassificationof5gwaveformmodulatedsignalsusingdeepresidualnetworks AT randajabeur automaticclassificationof5gwaveformmodulatedsignalsusingdeepresidualnetworks |