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APPLICATION OF HEURISTIC LEARNING SPECTRUM SENSING IN COGNITIVE RADIO NETWORK
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | YOUNG-JUNE CHOI | - |
| dc.contributor.author | RUKMAN RINALDY ARDYANSYAH | - |
| dc.date.accessioned | 2022-11-29T02:32:00Z | - |
| dc.date.available | 2022-11-29T02:32:00Z | - |
| dc.date.issued | 2020-02 | - |
| dc.identifier.other | 29933 | - |
| dc.identifier.uri | https://dspace.ajou.ac.kr/handle/2018.oak/19481 | - |
| dc.description | 학위논문(석사)--아주대학교 일반대학원 :컴퓨터공학과,2020. 2 | - |
| dc.description.tableofcontents | CHAPTER I. INTRODUCTION 1 A. COGNITIVE RADIO NETWORK 1 B. MOTIVATION 2 C. SPECTRUM SENSING 3 D. CONTRIBUTIONS AND OUTLINES 5 CHAPTER II. BACKGROUND DISCUSSION 7 A. RELATED WORKS 7 B. FRAME STRUCTURE 9 C. PRIMARY USER MODEL 12 D. SPECTRUM SENSING: ENERGY DETECTION (ED) 14 E. PREDICTION MODEL 15 CHAPTER III. HEURISTIC LEARNING SPECTRUM SENSING 18 A. SYSTEM MODEL 18 B. MAIN IDEA 19 C. PREDICTION RELIABILITY VALUE (PRV) 21 D. UPDATING PREDICTION DATA 23 E. ERROR ON UPDATING DATA AND PRV 24 CHAPTER IV. DISCUSSION AND RESULT 25 A. SIMULATION 25 B. RESULT 26 CHAPTER V. CONCLUSION AND FUTURE WORK 34 A. CONCLUSION 34 B. FUTURE WORK 34 | - |
| dc.language.iso | eng | - |
| dc.publisher | The Graduate School, Ajou University | - |
| dc.rights | 아주대학교 논문은 저작권에 의해 보호받습니다. | - |
| dc.title | APPLICATION OF HEURISTIC LEARNING SPECTRUM SENSING IN COGNITIVE RADIO NETWORK | - |
| dc.type | Thesis | - |
| dc.contributor.affiliation | 아주대학교 일반대학원 | - |
| dc.contributor.department | 일반대학원 컴퓨터공학과 | - |
| dc.date.awarded | 2020. 2 | - |
| dc.description.degree | Master | - |
| dc.identifier.localId | 1138505 | - |
| dc.identifier.uci | I804:41038-000000029933 | - |
| dc.identifier.url | http://dcoll.ajou.ac.kr:9080/dcollection/common/orgView/000000029933 | - |
| dc.subject.keyword | SPECTRUM SENSING | - |
| dc.description.alternativeAbstract | The cognitive radio is proposed to address the problem of spectrum scarcity in the current condition where the utilization of the spectrums is imbalance. The users in overutilized spectrum may access the other licensed spectrum opportunistically without causing harmful interference to the licensed user. To reduce the interference harmfulness, the unlicensed users have to detect the presence of the licensed user which is called spectrum sensing process. There are many problems in spectrum sensing process. Many works have been done to address the energy consumption problem and also increase the detection performance. There are several works have been done by using heuristic approach and also learning approach. Heuristic approaches mostly suffer from unoptimized results where the learning approaches suffer from long learning time and also the needed for prediction data. In this work, we propose the combination of prediction and heuristic approach where the users learn when to use the proper approach to increase the detection performance and also reduce the energy consumption. | - |
- Appears in Collections:
- Graduate School of Ajou University > Department of Computer Engineering > 3. Theses(Master)
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