Cognitive radio for enhancing spectral efficiency over wireless communications
In today's wireless networks, cognitive radio (CR) stands out as a crucial technology for efficiently managing limited spectrum resources. By adapting intelligently to changing environments, CR ensures optimal spectrum usage. This dissertation focuses on improving spectrum efficiency in CR n...
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| Other Authors: | |
| Format: | Thesis-Master by Coursework |
| Language: | English |
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Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/179101 |
| _version_ | 1824453505704787968 |
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| author | Xu, Xuanzhi |
| author2 | Li Kwok Hung |
| author_facet | Li Kwok Hung Xu, Xuanzhi |
| author_sort | Xu, Xuanzhi |
| collection | NTU |
| description | In today's wireless networks, cognitive radio (CR) stands out as a crucial technology
for efficiently managing limited spectrum resources. By adapting intelligently to
changing environments, CR ensures optimal spectrum usage. This dissertation focuses
on improving spectrum efficiency in CR networks through cooperative spectrum
sensing (CSS). By addressing the challenges involved, the study aims to enhance the
feasibility of CSS-based CR networks, evaluating key metrics like detection reliability
and transmission throughput.
Beginning with an exploration of CR's fundamental concepts and the critical role of
spectrum sensing, the dissertation conducts a comprehensive review of existing
literature and spectrum sensing methodologies, primarily focusing on energy detection
methods.
Moreover, the dissertation extends its analysis to spectrum sensing strategies within
CR networks. By considering the standalone sensing and cooperative sensing, the
research explores algorithms to maximize spectrum efficiency while ensuring the
protection of primary users' signals. Through systematic experimentation and analysis,
the study evaluates the performance of various spectrum sensing techniques,
considering factors such as variable parameters and the number of sensing samples.
Besides, By applying the machine learning (ML) model in CSS, the detection accuracy
and efficiency of CR systems have been enhanced. After simulation on different ML
techniques, the support vector machine (SVM) presents a better training model
compared with other ML techniques such as multilayer perceptron (MLP) and Naive
Bayes (NB), especially at low false alarm probability.
In conclusion, this research contributes to advancing the field of cognitive radio by
providing insights into effective spectrum sensing strategies. The findings offer
valuable implications for improving spectrum efficiency in wireless communication
networks. |
| first_indexed | 2025-02-19T03:07:29Z |
| format | Thesis-Master by Coursework |
| id | ntu-10356/179101 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:07:29Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1791012024-07-19T15:44:00Z Cognitive radio for enhancing spectral efficiency over wireless communications Xu, Xuanzhi Li Kwok Hung School of Electrical and Electronic Engineering EKHLI@ntu.edu.sg Engineering Cognitive radio Spectrum sensing Energy detection Machine learning Support vector machine Multilayer perceptron Naive Bayes In today's wireless networks, cognitive radio (CR) stands out as a crucial technology for efficiently managing limited spectrum resources. By adapting intelligently to changing environments, CR ensures optimal spectrum usage. This dissertation focuses on improving spectrum efficiency in CR networks through cooperative spectrum sensing (CSS). By addressing the challenges involved, the study aims to enhance the feasibility of CSS-based CR networks, evaluating key metrics like detection reliability and transmission throughput. Beginning with an exploration of CR's fundamental concepts and the critical role of spectrum sensing, the dissertation conducts a comprehensive review of existing literature and spectrum sensing methodologies, primarily focusing on energy detection methods. Moreover, the dissertation extends its analysis to spectrum sensing strategies within CR networks. By considering the standalone sensing and cooperative sensing, the research explores algorithms to maximize spectrum efficiency while ensuring the protection of primary users' signals. Through systematic experimentation and analysis, the study evaluates the performance of various spectrum sensing techniques, considering factors such as variable parameters and the number of sensing samples. Besides, By applying the machine learning (ML) model in CSS, the detection accuracy and efficiency of CR systems have been enhanced. After simulation on different ML techniques, the support vector machine (SVM) presents a better training model compared with other ML techniques such as multilayer perceptron (MLP) and Naive Bayes (NB), especially at low false alarm probability. In conclusion, this research contributes to advancing the field of cognitive radio by providing insights into effective spectrum sensing strategies. The findings offer valuable implications for improving spectrum efficiency in wireless communication networks. Master's degree 2024-07-18T02:33:38Z 2024-07-18T02:33:38Z 2024 Thesis-Master by Coursework Xu, X. (2024). Cognitive radio for enhancing spectral efficiency over wireless communications. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/179101 https://hdl.handle.net/10356/179101 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering Cognitive radio Spectrum sensing Energy detection Machine learning Support vector machine Multilayer perceptron Naive Bayes Xu, Xuanzhi Cognitive radio for enhancing spectral efficiency over wireless communications |
| title | Cognitive radio for enhancing spectral efficiency over wireless communications |
| title_full | Cognitive radio for enhancing spectral efficiency over wireless communications |
| title_fullStr | Cognitive radio for enhancing spectral efficiency over wireless communications |
| title_full_unstemmed | Cognitive radio for enhancing spectral efficiency over wireless communications |
| title_short | Cognitive radio for enhancing spectral efficiency over wireless communications |
| title_sort | cognitive radio for enhancing spectral efficiency over wireless communications |
| topic | Engineering Cognitive radio Spectrum sensing Energy detection Machine learning Support vector machine Multilayer perceptron Naive Bayes |
| url | https://hdl.handle.net/10356/179101 |
| work_keys_str_mv | AT xuxuanzhi cognitiveradioforenhancingspectralefficiencyoverwirelesscommunications |