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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 蘇炫榮(Hsuan-Jung Su) | |
| dc.contributor.author | Alan Shenghan Tsai | en |
| dc.contributor.author | 蔡昇翰 | zh_TW |
| dc.date.accessioned | 2021-06-16T03:40:15Z | - |
| dc.date.available | 2020-03-16 | |
| dc.date.copyright | 2015-03-16 | |
| dc.date.issued | 2015 | |
| dc.date.submitted | 2015-02-14 | |
| dc.identifier.citation | Bibliography
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/54867 | - |
| dc.description.abstract | 在最近幾年，互聯網 (M2M) 被廣泛應用於無限通訊系統中。機器本身會有功率限制，
處理和通訊能力也都會有限。壓縮感測技術可以避免傳多餘的資訊出去，進而壓縮傳送資料 量。在此篇論文中，在雙層架構下，我們於互聯網中針對隨機訊號源提出一個遠程的壓縮感 知架構，目的是壓縮閘道 (gateway) 的傳輸資料，並且我們將原本問題簡化成隨機壓縮感知 的問題。進一步，針對原本的系統中，在壓縮閘道到基地台中間，考慮通過一個白色雜訊的 通道的情況，讓系統更貼近真實生活的環境。 最後，我們找到兩種產生感知矩陣的方法。一個是奇異值分解共變異數矩陣 ( SVD Covariance Matrix)，這方法是使用機器跟機器之間在空間上存在的關聯性來壓縮傳送資料。 另一個是適應性統計壓縮感知(Adaptive Statistical Compressive Sensing)，這方法是參考過去傳 送過的資料，結合互信息來產生新的感知矩陣。將兩種方法應用於我們所提出的系統中，並 且驗證我們所選擇的這兩種感知矩陣在不同的系統下，可以達到傳統訊號傳輸所能達到的效 果優於之前的高斯 (Gaussian)或伯努力(Bernoulli) 感知矩陣。 | zh_TW |
| dc.description.abstract | In recent years, machine-to-machine (M2M) networks are widely con-
sidered in wireless communication systems. To avoid the transmission of redundant information to improve the data rate, compressive sensing is a promising tool to be considered. In this paper under the two-tier architec- ture, we propose a remote compressive sensing scheme for the M2M networks with stochastic sources to improve the data rate and formulate a statistical compressive sensing problem. First we propose to use the minimum mean square error estimator at the gateway and the base station to transform the problem as a noisy statistical CS. We derive the form of a optimal decoder by following MMSE estimation for the proposed scheme. Furthermore We find two ways, that can produce the sensing matrices. There are SVD covariance matrix(SCM) and adaptive statistical compressive sensing(ASCS). SCM is using machines covariance matrix to compressed the data by reducing the correlation between each machines. ASCS uses the previous measurements and the sensing matrices obtained in the past states and combines and com- bine the mutual information to become a new method of CS. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T03:40:15Z (GMT). No. of bitstreams: 1 ntu-104-R01942138-1.pdf: 606002 bytes, checksum: da8ef05ad7477816003e0d2d76eb551e (MD5) Previous issue date: 2015 | en |
| dc.description.tableofcontents | Contents
1 Introduction 1 1.1 Machine-to-Machine Networks . . . . . . . . . . . . . . . . . . 1 1.2 Compressive Sensing . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 Statistical Compressed Sensing of Gaussian Source . . . . . . 4 1.4 Why Use Gaussian Source . . . . . . . . . . . . . . . . . . . . 5 1.5 Adaptive Statistical Compressive Sensing . . . . . . . . . . . . 5 1.6 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.7 Notations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2 System Model 8 2.1 Proposed Remote Compressive Sensing Schemes for Machine- to-Machine Networks over noisy channel . . . . . . . . . . . . 9 2.2 Adaptive Remote Compressive Sensing Schemes for M2M Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 3 Statistical Compressive Sensing for Gaussian Signals 15 3.1 Previous Research Results . . . . . . . . . . . . . . . . . . . . 15 3.1.1 The Restrictive Isometry Property (RIP) . . . . . . . . 15 3.1.2 Random Sensing Matrices that satisfies the RIP . . . . 19 3.2 The Optimal Decoder for Gaussian Signals . . . . . . . . . . . 21 3.3 Design Sensing Matrix . . . . . . . . . . . . . . . . . . . . . . 25 3.3.1 Mean Square Error and Mutual Information in Gaus- sian Channels . . . . . . . . . . . . . . . . . . . . . . . 25 3.3.2 SVD Covariance Sensing Matrix . . . . . . . . . . . . . 28 3.4 Adaptive Statistical Compressive Sensing . . . . . . . . . . . . 31 4 Simulation Results 35 4.1 Simulation Environment . . . . . . . . . . . . . . . . . . . . . 35 4.2 Performance Comparison . . . . . . . . . . . . . . . . . . . . . 36 4.2.1 The overall performance . . . . . . . . . . . . . . . . . 37 4.2.2 Remote Compressive Sensing Over Noisy Channel . . . 38 4.2.3 Remote Compressive Sensing with SVD Covariance Ma- trix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 4.2.4 Adaptive Remote Compressive Sensing . . . . . . . . . 42 5 Conclusions 43 6 Future Works 45 Bibliography 46 | |
| dc.language.iso | en | |
| dc.subject | 適應性統計壓縮感知 | zh_TW |
| dc.subject | 互聯網 | zh_TW |
| dc.subject | 毛細管網路 | zh_TW |
| dc.subject | 壓縮感知 | zh_TW |
| dc.subject | 奇異值分解 | zh_TW |
| dc.subject | 共變異數矩陣 | zh_TW |
| dc.subject | Compressive sensing | en |
| dc.subject | Adaptive statistical compressive sensing | en |
| dc.subject | SVD covariance matrix | en |
| dc.subject | Mutual information | en |
| dc.subject | Noisy channel | en |
| dc.subject | Machine-to-Machine networks | en |
| dc.title | 使用遠程壓縮感測技術在有雜訊的物聯網 | zh_TW |
| dc.title | Remote Compressive Sensing for Noisy Machine-to-Machine Networks | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 103-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 蘇柏青,林秉勳 | |
| dc.subject.keyword | 互聯網,毛細管網路,壓縮感知,奇異值分解,共變異數矩陣,適應性統計壓縮感知, | zh_TW |
| dc.subject.keyword | Machine-to-Machine networks,Compressive sensing,Noisy channel,Mutual information,SVD covariance matrix,Adaptive statistical compressive sensing, | en |
| dc.relation.page | 55 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2015-02-14 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電信工程學研究所 | zh_TW |
| 顯示於系所單位： | 電信工程學研究所 | |
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