Random, Persistent, and Adaptive Spectrum Sensing Strategies for Multiband Spectrum Sensing in Cognitive Radio Networks With Secondary User Hardware Limitation
In this paper, we consider hardware limitation at the secondary user, which makes multiband (wideband) spectrum sensing more challenging. Under secondary user (SU) hardware limitation, the SU can only sense a small portion of the multiband spectrum for a given time period, which introduces a design...
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| Format: | Article |
| Language: | English |
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IEEE
2017-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/7999183/ |
| _version_ | 1818853883249688576 |
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| author | Tianyi Xiong Zan Li Yu-Dong Yao Peihan Qi |
| author_facet | Tianyi Xiong Zan Li Yu-Dong Yao Peihan Qi |
| author_sort | Tianyi Xiong |
| collection | DOAJ |
| description | In this paper, we consider hardware limitation at the secondary user, which makes multiband (wideband) spectrum sensing more challenging. Under secondary user (SU) hardware limitation, the SU can only sense a small portion of the multiband spectrum for a given time period, which introduces a design issue of selecting subchannels to sense at a given time. A random spectrum sensing strategy (RSSS) is presented to select the subchannels to sense in a totally random fashion. With the Markov assumption of the primary user (PU) behavior, a persistent spectrum sensing strategy (PSSS) is proposed to take advantage of the PU traffic patterns in determining the channels to sense. Theoretical and simulation results show that RSSS and PSSS display different performance in different ranges of PU traffic parameters. We finally propose an adaptive spectrum sensing strategy (ASSS), which determines whether to use RSSS or PSSS for spectrum sensing at a given time based on the estimated PU traffic parameters. Numerical results under various system parameters are presented to evaluate the performance of RSSS, PSSS, and ASSS. The ASSS is shown to gain the advantages of both RSSS and PSSS in different ranges of PU traffic parameters and provide more available subchannels for SU. |
| first_indexed | 2024-12-19T07:43:53Z |
| format | Article |
| id | doaj.art-8ca3a942e054403e9f254bb4b888ccbd |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T07:43:53Z |
| publishDate | 2017-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-8ca3a942e054403e9f254bb4b888ccbd2022-12-21T20:30:24ZengIEEEIEEE Access2169-35362017-01-015148541486610.1109/ACCESS.2017.27348117999183Random, Persistent, and Adaptive Spectrum Sensing Strategies for Multiband Spectrum Sensing in Cognitive Radio Networks With Secondary User Hardware LimitationTianyi Xiong0https://orcid.org/0000-0002-5102-928XZan Li1Yu-Dong Yao2Peihan Qi3State Key Laboratory of Integrated Service Networks, Xidian University, Xi’an, ChinaState Key Laboratory of Integrated Service Networks, Xidian University, Xi’an, ChinaDepartment of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, USAState Key Laboratory of Integrated Service Networks, Xidian University, Xi’an, ChinaIn this paper, we consider hardware limitation at the secondary user, which makes multiband (wideband) spectrum sensing more challenging. Under secondary user (SU) hardware limitation, the SU can only sense a small portion of the multiband spectrum for a given time period, which introduces a design issue of selecting subchannels to sense at a given time. A random spectrum sensing strategy (RSSS) is presented to select the subchannels to sense in a totally random fashion. With the Markov assumption of the primary user (PU) behavior, a persistent spectrum sensing strategy (PSSS) is proposed to take advantage of the PU traffic patterns in determining the channels to sense. Theoretical and simulation results show that RSSS and PSSS display different performance in different ranges of PU traffic parameters. We finally propose an adaptive spectrum sensing strategy (ASSS), which determines whether to use RSSS or PSSS for spectrum sensing at a given time based on the estimated PU traffic parameters. Numerical results under various system parameters are presented to evaluate the performance of RSSS, PSSS, and ASSS. The ASSS is shown to gain the advantages of both RSSS and PSSS in different ranges of PU traffic parameters and provide more available subchannels for SU.https://ieeexplore.ieee.org/document/7999183/Adaptive spectrum sensingcognitive radiosubchannel selectionwideband spectrum sensingMarkov model |
| spellingShingle | Tianyi Xiong Zan Li Yu-Dong Yao Peihan Qi Random, Persistent, and Adaptive Spectrum Sensing Strategies for Multiband Spectrum Sensing in Cognitive Radio Networks With Secondary User Hardware Limitation IEEE Access Adaptive spectrum sensing cognitive radio subchannel selection wideband spectrum sensing Markov model |
| title | Random, Persistent, and Adaptive Spectrum Sensing Strategies for Multiband Spectrum Sensing in Cognitive Radio Networks With Secondary User Hardware Limitation |
| title_full | Random, Persistent, and Adaptive Spectrum Sensing Strategies for Multiband Spectrum Sensing in Cognitive Radio Networks With Secondary User Hardware Limitation |
| title_fullStr | Random, Persistent, and Adaptive Spectrum Sensing Strategies for Multiband Spectrum Sensing in Cognitive Radio Networks With Secondary User Hardware Limitation |
| title_full_unstemmed | Random, Persistent, and Adaptive Spectrum Sensing Strategies for Multiband Spectrum Sensing in Cognitive Radio Networks With Secondary User Hardware Limitation |
| title_short | Random, Persistent, and Adaptive Spectrum Sensing Strategies for Multiband Spectrum Sensing in Cognitive Radio Networks With Secondary User Hardware Limitation |
| title_sort | random persistent and adaptive spectrum sensing strategies for multiband spectrum sensing in cognitive radio networks with secondary user hardware limitation |
| topic | Adaptive spectrum sensing cognitive radio subchannel selection wideband spectrum sensing Markov model |
| url | https://ieeexplore.ieee.org/document/7999183/ |
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