Spectrum Sensing Based on Hybrid Spectrum Handoff in Cognitive Radio Networks
The rapid advancement of wireless communication combined with insufficient spectrum exploitation opens the door for the expansion of novel wireless services. Cognitive radio network (CRN) technology makes it possible to periodically access the open spectrum bands, which in turn improves the effectiv...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-08-01
|
| Series: | Entropy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1099-4300/25/9/1285 |
| _version_ | 1797580192872398848 |
|---|---|
| author | Lakshminarayanan Vaduganathan Shubhangi Neware Przemysław Falkowski-Gilski Parameshachari Bidare Divakarachari |
| author_facet | Lakshminarayanan Vaduganathan Shubhangi Neware Przemysław Falkowski-Gilski Parameshachari Bidare Divakarachari |
| author_sort | Lakshminarayanan Vaduganathan |
| collection | DOAJ |
| description | The rapid advancement of wireless communication combined with insufficient spectrum exploitation opens the door for the expansion of novel wireless services. Cognitive radio network (CRN) technology makes it possible to periodically access the open spectrum bands, which in turn improves the effectiveness of CRNs. Spectrum sensing (SS), which allows unauthorized users to locate open spectrum bands, plays a fundamental part in CRNs. A precise approximation of the power spectrum is essential to accomplish this. On the assumption that each SU’s parameter vector contains some globally and partially shared parameters, spectrum sensing is viewed as a parameter estimation issue. Distributed and cooperative spectrum sensing (CSS) is a key component of this concept. This work introduces a new component-specific cooperative spectrum sensing model (CSCSSM) in CRNs considering the amplitude and phase components of the input signal including Component Specific Adaptive Estimation (CSAE) for mean squared deviation (MSD) formulation. The proposed concept ensures minimum information loss compared to the traditional methods that consider error calculation among the direct signal vectors. The experimental results and performance analysis prove the robustness and efficiency of the proposed work over the traditional methods. |
| first_indexed | 2024-03-10T22:46:57Z |
| format | Article |
| id | doaj.art-10016215605442749ed0cafcedd74691 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T22:46:57Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-10016215605442749ed0cafcedd746912023-11-19T10:35:29ZengMDPI AGEntropy1099-43002023-08-01259128510.3390/e25091285Spectrum Sensing Based on Hybrid Spectrum Handoff in Cognitive Radio NetworksLakshminarayanan Vaduganathan0Shubhangi Neware1Przemysław Falkowski-Gilski2Parameshachari Bidare Divakarachari3Department of Electrical and Electronics Engineering, Dr. Mahalingam College of Engineering and Technology, Pollachi 642003, IndiaDepartment of Computer Science and Engineering, Shri Ramdeobaba College of Engineering and Management, Nagpur 440013, IndiaFaculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Narutowicza 11/12, 80-233 Gdansk, PolandDepartment of Electronics and Communication Engineering, Nitte Meenakshi Institute of Technology, Bengaluru 560064, IndiaThe rapid advancement of wireless communication combined with insufficient spectrum exploitation opens the door for the expansion of novel wireless services. Cognitive radio network (CRN) technology makes it possible to periodically access the open spectrum bands, which in turn improves the effectiveness of CRNs. Spectrum sensing (SS), which allows unauthorized users to locate open spectrum bands, plays a fundamental part in CRNs. A precise approximation of the power spectrum is essential to accomplish this. On the assumption that each SU’s parameter vector contains some globally and partially shared parameters, spectrum sensing is viewed as a parameter estimation issue. Distributed and cooperative spectrum sensing (CSS) is a key component of this concept. This work introduces a new component-specific cooperative spectrum sensing model (CSCSSM) in CRNs considering the amplitude and phase components of the input signal including Component Specific Adaptive Estimation (CSAE) for mean squared deviation (MSD) formulation. The proposed concept ensures minimum information loss compared to the traditional methods that consider error calculation among the direct signal vectors. The experimental results and performance analysis prove the robustness and efficiency of the proposed work over the traditional methods.https://www.mdpi.com/1099-4300/25/9/1285cognitive radio networkscomponent-specific adaptive estimationprimary userspower spectrumspectrum sensing |
| spellingShingle | Lakshminarayanan Vaduganathan Shubhangi Neware Przemysław Falkowski-Gilski Parameshachari Bidare Divakarachari Spectrum Sensing Based on Hybrid Spectrum Handoff in Cognitive Radio Networks Entropy cognitive radio networks component-specific adaptive estimation primary users power spectrum spectrum sensing |
| title | Spectrum Sensing Based on Hybrid Spectrum Handoff in Cognitive Radio Networks |
| title_full | Spectrum Sensing Based on Hybrid Spectrum Handoff in Cognitive Radio Networks |
| title_fullStr | Spectrum Sensing Based on Hybrid Spectrum Handoff in Cognitive Radio Networks |
| title_full_unstemmed | Spectrum Sensing Based on Hybrid Spectrum Handoff in Cognitive Radio Networks |
| title_short | Spectrum Sensing Based on Hybrid Spectrum Handoff in Cognitive Radio Networks |
| title_sort | spectrum sensing based on hybrid spectrum handoff in cognitive radio networks |
| topic | cognitive radio networks component-specific adaptive estimation primary users power spectrum spectrum sensing |
| url | https://www.mdpi.com/1099-4300/25/9/1285 |
| work_keys_str_mv | AT lakshminarayananvaduganathan spectrumsensingbasedonhybridspectrumhandoffincognitiveradionetworks AT shubhangineware spectrumsensingbasedonhybridspectrumhandoffincognitiveradionetworks AT przemysławfalkowskigilski spectrumsensingbasedonhybridspectrumhandoffincognitiveradionetworks AT parameshacharibidaredivakarachari spectrumsensingbasedonhybridspectrumhandoffincognitiveradionetworks |