Fast Two-Step Energy Detection for Spectrum Sensing
Spectrum sensing is one of the key tasks in cognitive radio. This paper proposes a fast two-step energy detection (FED) algorithm for spectrum sensing via improving the sampling process of conventional energy detection (CED). The algorithm adaptively selects N-point or 2N-point sampling by comparing...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2015/591627 |
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| _version_ | 1832568494475968512 |
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| author | Meiling Lai Shengliang Peng Xi Yang Lin Zhou |
| author_facet | Meiling Lai Shengliang Peng Xi Yang Lin Zhou |
| author_sort | Meiling Lai |
| collection | DOAJ |
| description | Spectrum sensing is one of the key tasks in cognitive radio. This paper proposes a fast two-step energy detection (FED) algorithm for spectrum sensing via improving the sampling process of conventional energy detection (CED). The algorithm adaptively selects N-point or 2N-point sampling by comparing its observed energy with prefixed double thresholds, and thereby is superior in sampling time and detection speed. Moreover, under the constraint of constant false alarm, this paper optimizes the thresholds from maximizing detection probability point of view. Theoretical analyses and simulation results show that, compared with CED, the proposed FED can achieve significant gain in detection speed at the expense of slight accuracy loss. Specifically, within high signal-to-noise ratio regions, as much as 25% of samples can be reduced. |
| format | Article |
| id | doaj-art-486dccc28cec4796b455097804550b38 |
| institution | Kabale University |
| issn | 2090-0147 2090-0155 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-486dccc28cec4796b455097804550b382025-02-03T00:58:56ZengWileyJournal of Electrical and Computer Engineering2090-01472090-01552015-01-01201510.1155/2015/591627591627Fast Two-Step Energy Detection for Spectrum SensingMeiling Lai0Shengliang Peng1Xi Yang2Lin Zhou3Institute of Communications Technology, Huaqiao University, Xiamen 361021, ChinaInstitute of Communications Technology, Huaqiao University, Xiamen 361021, ChinaSchool of Information Science and Technology, Jishou University, Jishou 416000, ChinaInstitute of Communications Technology, Huaqiao University, Xiamen 361021, ChinaSpectrum sensing is one of the key tasks in cognitive radio. This paper proposes a fast two-step energy detection (FED) algorithm for spectrum sensing via improving the sampling process of conventional energy detection (CED). The algorithm adaptively selects N-point or 2N-point sampling by comparing its observed energy with prefixed double thresholds, and thereby is superior in sampling time and detection speed. Moreover, under the constraint of constant false alarm, this paper optimizes the thresholds from maximizing detection probability point of view. Theoretical analyses and simulation results show that, compared with CED, the proposed FED can achieve significant gain in detection speed at the expense of slight accuracy loss. Specifically, within high signal-to-noise ratio regions, as much as 25% of samples can be reduced.http://dx.doi.org/10.1155/2015/591627 |
| spellingShingle | Meiling Lai Shengliang Peng Xi Yang Lin Zhou Fast Two-Step Energy Detection for Spectrum Sensing Journal of Electrical and Computer Engineering |
| title | Fast Two-Step Energy Detection for Spectrum Sensing |
| title_full | Fast Two-Step Energy Detection for Spectrum Sensing |
| title_fullStr | Fast Two-Step Energy Detection for Spectrum Sensing |
| title_full_unstemmed | Fast Two-Step Energy Detection for Spectrum Sensing |
| title_short | Fast Two-Step Energy Detection for Spectrum Sensing |
| title_sort | fast two step energy detection for spectrum sensing |
| url | http://dx.doi.org/10.1155/2015/591627 |
| work_keys_str_mv | AT meilinglai fasttwostepenergydetectionforspectrumsensing AT shengliangpeng fasttwostepenergydetectionforspectrumsensing AT xiyang fasttwostepenergydetectionforspectrumsensing AT linzhou fasttwostepenergydetectionforspectrumsensing |