Cognitive link adaptation via modulation scheme classification in narrowband networks under AWGN and SUI channel conditions
Abstract The demand for faster data transfer rates rises along with the number of mobile devices, such as smartphones and IoT gadgets, which makes the radio spectrum more crowded. The forthcoming 5G wireless communication technology seeks to significantly enhance data speeds and spectrum efficiency...
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-12277-z |
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| author | Fatima Ismail Sajid Gul Khawaja Asad Mansoor Khan Umer Hameed Shah Muhammad Usman Akram Arslan Shaukat |
| author_facet | Fatima Ismail Sajid Gul Khawaja Asad Mansoor Khan Umer Hameed Shah Muhammad Usman Akram Arslan Shaukat |
| author_sort | Fatima Ismail |
| collection | DOAJ |
| description | Abstract The demand for faster data transfer rates rises along with the number of mobile devices, such as smartphones and IoT gadgets, which makes the radio spectrum more crowded. The forthcoming 5G wireless communication technology seeks to significantly enhance data speeds and spectrum efficiency by dynamically adjusting to fluctuating channel conditions. This research presents a new approach in the form of a hierarchical machine learning system for automation of modulation classification and adaptive parameter selection that optimizes spectral efficiency for different communication channels. A hierarchical approach is adopted in place of traditional methods that classify modulation schemes as separate entities. This method first predicts the modulation type (e.g., PSK, FSK, CPM), and subsequently determines the optimal parameters (M, h, L) corresponding to the identified channel conditions. During experimentation, seven modulation schemes were tested (2-PSK, 4-PSK, 8-PSK, 2-FSK, 4-FSK, 8-FSK, and CPM) for different modulation orders ( $$M~=~[2,~4,~8]$$ ) and spectral efficiencies $$(h~=~[1/2,~1/4,~1/8,~1/16])$$ as well as for overlap factors $$(L~=~[1,~2,~3])$$ . A detailed MATLAB simulation was built and signals were transmitted over different channels (AWGN and SUI Stanford University Interin) for evaluation over different frequency ranges. Performance of our proposed hierarchical framework was examined based on the Bit Error Rate (BER) and achievable data rate in different signal-to-noise ratio (SNR) situations. The accuracy achieved by our proposed hierarchical classifier was 98.57%, proving effectiveness in adaptive modulation selection. These achievements suggest plainly how cognitive radio systems and next generation wireless networks can benefit by the real-time spectrum adaptation and improvement in data reliability in transmission. |
| format | Article |
| id | doaj-art-9696c3d29bbb4f29a8989bd001419c4a |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-9696c3d29bbb4f29a8989bd001419c4a2025-08-20T03:04:38ZengNature PortfolioScientific Reports2045-23222025-07-0115111710.1038/s41598-025-12277-zCognitive link adaptation via modulation scheme classification in narrowband networks under AWGN and SUI channel conditionsFatima Ismail0Sajid Gul Khawaja1Asad Mansoor Khan2Umer Hameed Shah3Muhammad Usman Akram4Arslan Shaukat5College of Electrical and Mechanical Engineering, National University of Sciences and TechnologyFaculty of Engineering, Electrical, Computer and Biomedical Engineering Department, Abu Dhabi UniversityCollege of Electrical and Mechanical Engineering, National University of Sciences and TechnologyDepartment of Mechanical Engineering and Artificial Intelligence Research Center, College of Engineering and Information Technology, Ajman UniversityCollege of Electrical and Mechanical Engineering, National University of Sciences and TechnologyCollege of Electrical and Mechanical Engineering, National University of Sciences and TechnologyAbstract The demand for faster data transfer rates rises along with the number of mobile devices, such as smartphones and IoT gadgets, which makes the radio spectrum more crowded. The forthcoming 5G wireless communication technology seeks to significantly enhance data speeds and spectrum efficiency by dynamically adjusting to fluctuating channel conditions. This research presents a new approach in the form of a hierarchical machine learning system for automation of modulation classification and adaptive parameter selection that optimizes spectral efficiency for different communication channels. A hierarchical approach is adopted in place of traditional methods that classify modulation schemes as separate entities. This method first predicts the modulation type (e.g., PSK, FSK, CPM), and subsequently determines the optimal parameters (M, h, L) corresponding to the identified channel conditions. During experimentation, seven modulation schemes were tested (2-PSK, 4-PSK, 8-PSK, 2-FSK, 4-FSK, 8-FSK, and CPM) for different modulation orders ( $$M~=~[2,~4,~8]$$ ) and spectral efficiencies $$(h~=~[1/2,~1/4,~1/8,~1/16])$$ as well as for overlap factors $$(L~=~[1,~2,~3])$$ . A detailed MATLAB simulation was built and signals were transmitted over different channels (AWGN and SUI Stanford University Interin) for evaluation over different frequency ranges. Performance of our proposed hierarchical framework was examined based on the Bit Error Rate (BER) and achievable data rate in different signal-to-noise ratio (SNR) situations. The accuracy achieved by our proposed hierarchical classifier was 98.57%, proving effectiveness in adaptive modulation selection. These achievements suggest plainly how cognitive radio systems and next generation wireless networks can benefit by the real-time spectrum adaptation and improvement in data reliability in transmission.https://doi.org/10.1038/s41598-025-12277-z |
| spellingShingle | Fatima Ismail Sajid Gul Khawaja Asad Mansoor Khan Umer Hameed Shah Muhammad Usman Akram Arslan Shaukat Cognitive link adaptation via modulation scheme classification in narrowband networks under AWGN and SUI channel conditions Scientific Reports |
| title | Cognitive link adaptation via modulation scheme classification in narrowband networks under AWGN and SUI channel conditions |
| title_full | Cognitive link adaptation via modulation scheme classification in narrowband networks under AWGN and SUI channel conditions |
| title_fullStr | Cognitive link adaptation via modulation scheme classification in narrowband networks under AWGN and SUI channel conditions |
| title_full_unstemmed | Cognitive link adaptation via modulation scheme classification in narrowband networks under AWGN and SUI channel conditions |
| title_short | Cognitive link adaptation via modulation scheme classification in narrowband networks under AWGN and SUI channel conditions |
| title_sort | cognitive link adaptation via modulation scheme classification in narrowband networks under awgn and sui channel conditions |
| url | https://doi.org/10.1038/s41598-025-12277-z |
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