請用此 Handle URI 來引用此文件：
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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 劉俊麟(Chun-Lin Liu) | |
| dc.contributor.author | Ting-Yu Hsieh | en |
| dc.contributor.author | 謝珽宇 | zh_TW |
| dc.date.accessioned | 2022-11-23T09:04:47Z | - |
| dc.date.available | 2021-10-04 | |
| dc.date.available | 2022-11-23T09:04:47Z | - |
| dc.date.copyright | 2021-10-04 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-09-15 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/79597 | - |
| dc.description.abstract | 隨著壓縮感知理論的興起，基於次Nyquist取樣的寬頻頻譜感知再訊號處理領域變成一個熱門的研究主題。然而，大部分已知的方法並沒有事先確認在觀測的頻寬內是否存在主要用戶的訊號而是直接將主要用戶訊號的頻譜復原。而如果整個頻寬內並沒有包含任何訊號並且接收到訊號只包含雜訊的話，直接尋求訊號頻譜可能會導致錯誤的估測結果並且浪費運算資源。為了解決這些問題，PCER探測器被提了出來。然而我們發現PCER探測器並不適用於訊號有包含高斯程序的情況。因此我們提出了一個新的預決策探測器，新的探測器能夠處理上述PCER探測器無法應付的情況。在進行預決策後，會接著進行頻譜估測。我們利用Multitaper的概念提出了一個新的頻譜估測方法，經過一些數學推導後，我們發現欲估測的頻譜可以由簡單的最小平方法得到。整體而言，整個寬頻頻譜感知系統可以分為三個部分，次Nyquist取樣，預決策，頻譜估測。 這篇論文主要分成兩個部分。第一個部分為提出的預決策探測器的介紹，我們利用次Nyquist取樣點來得到我們的檢驗統計量，再進行一些數學運算後，可以得到最後的決策結果。我們也推導出了決策閾值和偵測機率的解析解。模擬結果顯示了新的探測器能在很大的雜訊比範圍內診測到主要用戶訊號的存在，並且能夠解決高斯程序無法被偵測的問題。 第二個部分為提出的頻譜估測方法的介紹。利用Multitaper的觀念，我們推導出收到的次Nyquist取樣點和訊號頻譜之間的關係，並且我們發現頻譜能夠利用最小平方法來進行還原。為了使估測的頻譜具有唯一性，我們也介紹了基於最小尺刻度問題的一種Multicoset取樣方式。模擬結果驗證了理論推導的正確性，並且顯示出了提出的方法能在雜訊比很低的情況下有很好的性能，能夠有效的對抗雜訊。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-23T09:04:47Z (GMT). No. of bitstreams: 1 U0001-1409202103160900.pdf: 5391833 bytes, checksum: a45229653ff159e9de2ce8c6644507ab (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | Contents Verification Letter from the Oral Examination Committee . . . . . . . . . . . . i 摘要 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii Abstract. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v Contents. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii List of Figures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .xi List of Tables. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .xv Chapter 1: Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . .1 Chapter 2: Preliminaries. . . . . . . . . . . . . . . . . . . . . . . . . . . . .7 2.1 System model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.1.1 Signal Model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.1.2 SubNyquist Sampling. . . . . . . . . . . . . . . . . . . . . . . . 8 2.1.3 Sampling Pattern Design. . . . . . . . . . . . . . . . . . . . . . . 11 2.2 Review of Multitaper. . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.2.1 SecondOrder Statistics and their Spectral Representation. . . . . . 14 2.2.2 Multitaper Spectral Estimate. . . . . . . . . . . . . . . . . . . . . 16 2.2.3 Tapers of Multitaper. . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.3 PCER Detector. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.4 OneBit Noise Estimator. . . . . . . . . . . . . . . . . . . . . . . . 26 2.4.1 OneBit Quantizer. . . . . . . . . . . . . . . . . . . . . . . . . . . 26 2.4.2 Noise Estimation using OneBit Quantizer. . . . . . . . . . . . . . 27 2.5 Other SubNyquist Spectrum Estimation Methods. . . . . . . . . . . 30 2.5.1 Associative ArrayBased Compressive Covariance Sensing. . . . . 31 2.5.2 Online Compressive Covariance Sensing. . . . . . . . . . . . . . . 33 Chapter 3: Predecision for Wideband Spectrum Sensing. . . . . . . . . . . . . . .35 3.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.2 Problem Statement. . . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.3 Proposed Detector. . . . . . . . . . . . . . . . . . . . . . . . . . . 39 3.4 False Alarm Probability Analysis. . . . . . . . . . . . . . . . . . . 41 3.5 Detection Probability Analysis. . . . . . . . . . . . . . . . . . . . . 45 3.6 Time Complexity Analysis. . . . . . . . . . . . . . . . . . . . . . . 48 3.7 Simulation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 3.7.1 Detection Performance under Different Noise Powers. . . . . . . . 50 3.7.2 Histograms of Different Methods. . . . . . . . . . . . . . . . . . . 53 3.7.3 ROC Curves of Different Methods. . . . . . . . . . . . . . . . . . 56 3.7.4 Detection Performance of Different Sample Measurements. . . . . 59 3.7.5 Detection Performance of Different Channel Numbers. . . . . . . . 60 3.7.6 Detection Performance of Different Signals. . . . . . . . . . . . . 62 3.7.7 Computational Time with Different Sample Measurements. . . . . 64 3.8 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 Chapter 4: Wideband Power Spectrum Estimation based on Multitaper. . . . . . . .67 4.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 4.2 Signal Model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 4.3 Proposed Algorithm. . . . . . . . . . . . . . . . . . . . . . . . . . 69 4.3.1 Construction of Eigenspectrum of Proposed Algorithm. . . . . . . 69 4.3.2 Power Spectrum Reconstruction using Least Squares Method. . . 70 4.3.3 Sampling Pattern for Proposed Algorithm. . . . . . . . . . . . . . 72 4.4 Power Spectrum Estimation with Additional Constraints. . . . . . . 76 4.5 Simulation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 4.5.1 The Estimated Power Spectrum Using Proposed Algorithm. . . . . 78 4.5.2 Estimation Performance at Different Sampling Time. . . . . . . . . 80 4.5.3 Estimation Performance under Different SNRs. . . . . . . . . . . . 81 4.5.4 Comparison of Time Complexity for Different Method. . . . . . . 82 4.6 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 Chapter 5: Simulation of Wideband Spectrum Sensing System. . . . . . . . . . . 85 5.1 Performance under Different Noise Powers. . . . . . . . . . . . . . 90 5.2 Performance of low Sample Measurements. . . . . . . . . . . . . . 93 5.3 Performance of low Compression Rate. . . . . . . . . . . . . . . . 97 5.4 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 Chapter 6: Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .101 Chapter 7: Future Work. . . . . . . . . . . . . . . . . . . . . . . . . . . . .103 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .105 | |
| dc.language.iso | en | |
| dc.title | 利用次Nyquist取樣方式於寬頻頻譜感知: 預決策與頻譜估計 | zh_TW |
| dc.title | Wideband Spectrum Sensing with Sub-Nyquist Sampling: Predecision and Spectrum Estimation | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 馮世邁(Hsin-Tsai Liu),蘇柏青(Chih-Yang Tseng) | |
| dc.subject.keyword | 寬頻頻譜感知,Multicoset取樣,預決策,Multitaper,最小平方法,尺刻度問題, | zh_TW |
| dc.subject.keyword | Wideband Spectrum Sensing,Multicoset Sampling,Predicision,Multitaper,Least-Squares Method,Sparse Ruler Problem, | en |
| dc.relation.page | 110 | |
| dc.identifier.doi | 10.6342/NTU202103159 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2021-09-16 | |
| 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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| U0001-1409202103160900.pdf | 5.27 MB | Adobe PDF | 檢視/開啟 |
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