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Weighted Cooperative Spectrum Sensing for Cognitive Radio Networks
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | Chaewoo Lee | - |
| dc.contributor.author | ATA, NURIA IBRAHIM | - |
| dc.date.accessioned | 2018-11-08T08:18:52Z | - |
| dc.date.available | 2018-11-08T08:18:52Z | - |
| dc.date.issued | 2015-08 | - |
| dc.identifier.other | 20361 | - |
| dc.identifier.uri | https://dspace.ajou.ac.kr/handle/2018.oak/12777 | - |
| dc.description | 학위논문(석사)--아주대학교 일반대학원 :전자공학과,2015. 8 | - |
| dc.description.tableofcontents | 1. Introduction ……………………………………………………………1 1.1 Background and Motivation …………………………………………1 1.2 Outline ………………………………………………………………4 2. Cognitive radio………………………………………………………6 2.1 Introduction…………………………………………………………6 2.2 Architecture of Cognitive radio……………………………………8 2.3 Application of Cognitive radio …………………………………10 3 Spectrum sensing ……………………………………………………13 3.1 Introduction …………………………………………………13 3.2 Local Spectrum Sensing ………………………………………13 3.3 Cooperative Spectrum Sensing………………………………19 3.3.1 Equal gain combining (EGC)……………………23 3.3.2 K-out-of-L fusion rule……………………………24 4 Weighting Method for Cooperative Spectrum Sensing …………26 4.1 Motivation of weighted cooperative spectrum sensing ………26 4.2 Related Work…………………………………………………27 4.3 Optimal Hard Decision Fusion ………………………………30 4.4 Proposed Decision weighting Method…………………………32 5 Simulation ……………………………………………………………38 5.1 Local sensing performance …………………………………39 5.2 Convergence of Estimated Weight …………………………41 5.3 Weighted Cooperative Spectrum Sensing performance ……47 6 Conclusion ……………………………………………………………50 6.1 Conclusion……………………………………………………50 Reference …………………………………………………………………52 | - |
| dc.language.iso | eng | - |
| dc.publisher | The Graduate School, Ajou University | - |
| dc.rights | 아주대학교 논문은 저작권에 의해 보호받습니다. | - |
| dc.title | Weighted Cooperative Spectrum Sensing for Cognitive Radio Networks | - |
| dc.type | Thesis | - |
| dc.contributor.affiliation | 아주대학교 일반대학원 | - |
| dc.contributor.department | 일반대학원 전자공학과 | - |
| dc.date.awarded | 2015. 8 | - |
| dc.description.degree | Master | - |
| dc.identifier.localId | 705460 | - |
| dc.identifier.url | http://dcoll.ajou.ac.kr:9080/dcollection/jsp/common/DcLoOrgPer.jsp?sItemId=000000020361 | - |
| dc.subject.keyword | Cognitive Radio technology | - |
| dc.description.alternativeAbstract | Following the rapidly growing customer demand for high speed data communication, spectrum scarcity has emerged as a major concern for various radio communication services such as Wi-Fi, Bluetooth, WSN, and Femtocells. However, according to the FCC 70% to 85 % of the allocated licensed spectrum is unused in time, space and frequency domain. Cognitive radio technology is a frequency agile radio which increases spectrum efficiency by allowing unlicensed or secondary devices to access the underutilized spectrum without causing harmful interference on legacy or primary users. Cognitive radio technology depends on spectrum sensing to detect vacant frequency bands. The accuracy of spectrum sensing is crucial in order to avoid interference on primary users. However, local sensing performance suffers under low signal to noise ratio AWGN channel, fading and shadowing environment. Thus cooperative spectrum sensing, has been recently introduced to counter the above problem. In cooperative spectrum sensing the local sensing data of multiple cognitive users is combined to make a more accurate global decision about target channel occupancy. Depending on distance between the secondary user and the primary transmitter, the quality of the received signal strength is diverse among cooperating cognitive users. In weighted cooperative spectrum sensing the local sensing decision of each secondary user is weighted by a factor proportional to its local sensing reliability, in order to improve the performance of cooperative spectrum. In practice the fusion center does not have prior knowledge on the local sensing reliability of cooperating secondary users, thus in this thesis adaptive optimal hard decision weighting method based on simple counting rule is introduced. This weighting algorithm estimates the local sensing reliability of each secondary user by counting the number of correct and wrong decisions made by each user relative to the final decision. Simulation results show that under steady state condition the estimated weight converges to the actual local sensing reliability of each user with slight estimation error. In addition, the error probability of the proposed weighted cooperative spectrum sensing scheme is lower than existing weighted cooperative spectrum sensing method. | - |
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- Graduate School of Ajou University > Department of Electronic Engineering > 3. Theses(Master)
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