repository
비트 단위 사후 확률 최대화에 기반한 탐욕적 신호 복구 알고리즘
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 홍송남 | - |
| dc.contributor.author | 채정민 | - |
| dc.date.accessioned | 2022-11-29T02:32:06Z | - |
| dc.date.available | 2022-11-29T02:32:06Z | - |
| dc.date.issued | 2020-02 | - |
| dc.identifier.other | 29996 | - |
| dc.identifier.uri | https://dspace.ajou.ac.kr/handle/2018.oak/19601 | - |
| dc.description | 학위논문(석사)--아주대학교 일반대학원 :전자공학과,2020. 2 | - |
| dc.description.tableofcontents | CHAPTER I. INTRODUCTION 1.1 Background and motivation 1.2 Thesis contributions 1.3 Thesis organization CHAPTER II. PRELIMINARIES 2.1 Notation 2.2 Problem formulation 2.3 The proposed greedy framework CHAPTER III. The Proposed B-MAP Proxy 3.1 Theorem 1 3.2 Corollary 1 3.1.1 Proof of Theorem 1 3.2.1 Proof of Corollary 1 CHAPTER IV. ASYMPTOTIC ANALYSIS FOR EXACT SUPPORT RECOVERY CHAPTER V. EXTENSIONS OF THE PROPOSED B-MAP PROXY 5.1 B-MAP proxy: Random non-zero values 5.2 Advanced Greedy Algorithms CHAPTER VI. NUMERICAL RESULTS 6.1 Binary sparse signal 6.2 Random sparse signal CHAPTER VII. CONCLUSION REFERENCES | - |
| dc.language.iso | eng | - |
| dc.publisher | The Graduate School, Ajou University | - |
| dc.rights | 아주대학교 논문은 저작권에 의해 보호받습니다. | - |
| dc.title | 비트 단위 사후 확률 최대화에 기반한 탐욕적 신호 복구 알고리즘 | - |
| dc.title.alternative | Jeongmin Chae | - |
| dc.type | Thesis | - |
| dc.contributor.affiliation | 아주대학교 일반대학원 | - |
| dc.contributor.alternativeName | Jeongmin Chae | - |
| dc.contributor.department | 일반대학원 전자공학과 | - |
| dc.date.awarded | 2020. 2 | - |
| dc.description.degree | Master | - |
| dc.identifier.localId | 1138691 | - |
| dc.identifier.uci | I804:41038-000000029996 | - |
| dc.identifier.url | http://dcoll.ajou.ac.kr:9080/dcollection/common/orgView/000000029996 | - |
| dc.subject.keyword | Compressive sensing | - |
| dc.subject.keyword | sparse signal recovery | - |
| dc.subject.keyword | support detection | - |
| dc.description.alternativeAbstract | This dissertation proposed a novel greedy algorithm for the support recovery of a sparse signal from a small number of noisy measurements. In the proposed algorithm, a new support index is identified for each iteration based on bit-wise maximum a posteriori (B-MAP) detection. Bit-Wise MAP detection is optimal in the sense of detecting one of the remaining support indices, provided that all the detected indices in the previous iterations are correct. Despite its optimality, it requires an expensive complexity for computing the maximization metric, (i.e., a posteriori probability of each remaining support) due to the marginalization of high-dimensional sparse vectors. We address this problem by presenting a good proxy, named B-MAP proxy, on the maximization metric which is accurate enough to find the maximum index, rather than an exact probability. Moreover, it is easily evaluated only using vector correlations as in orthogonal matching pursuit (OMP), but using completely different proxy matrices for maximization. We have demonstrated that the proposed B-MAP detection provides a significant gain compared with the existing methods such as OMP and MAP-OMP while maintaining the same complexity. Subsequently, we construct an advanced greedy algorithm based on B-MAP proxy by leveraging the idea of compressive sampling matching pursuit (CoSaMP) and subspace pursuit (SP). Simulation result shows that we show that our B-MAP algorithm outperforms OMP and MAP-OMP under the frameworks of the advanced greedy algorithms. | - |
- Appears in Collections:
- Graduate School of Ajou University > Department of Electronic Engineering > 3. Theses(Master)
- Files in This Item:
- There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.
-
- License
- STATISTICS
- Total Visit :3,634,515
- Total Download :1,804
- Today View :1,694
