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研究生:蔡舒韻
研究生(外文):Shu-Yun Tsai
論文名稱:應用總體經驗模態分解法之能量偵測器
論文名稱(外文):A Novel Energy Detection Method Based on Ensemble Empirical Mode Decomposition
指導教授:鄭献勳
指導教授(外文):Shiann-Shiun Jeng
學位類別:碩士
校院名稱:國立東華大學
系所名稱:電機工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
論文頁數:69
中文關鍵詞:感知無線電頻譜偵測能量偵測器總體經驗模態分解法
外文關鍵詞:Cognitive radioSpectrum sensingEnergy detectionEnsemble empirical mode decomposition
相關次數:
  • 被引用被引用:1
  • 點閱點閱:184
  • 評分評分:
  • 下載下載:10
  • 收藏至我的研究室書目清單書目收藏:0
感知無線電(cognitive radio, CR)為一種新的智慧型無線通訊技術,利用感知周圍的環境特徵因子,自動調整系統的發射和接收參數,如功率、頻率、調變等。感知無線電技術被用來解決低頻譜使用效率的問題,其中頻譜偵測為一項重要的關鍵技術。本論文中,我們使用的頻譜偵測技術為能量偵測器,大部分對於能量偵測器的研究都是基於已知通道環境中的雜訊功率資訊去設定門檻值,本論文則是在未知的雜訊環境中,使用總體經驗模態分解法(ensemble empirical mode decomposition, EEMD)解構出授權用戶的訊號及雜訊,設定一個具適應性的能量偵測器門檻值以改進能量偵測器之效能。
Cognitive radio(CR) is a novel smart wireless communication technology. To improve spectrum utilization efficiency, CR can detect the communication environment features to automatically adjust the transmitting and receiving parameters of a system, such as power, frequency, and modulation mode etc. The spectrum sensing is a key technology in the CR. In this thesis, we use energy detection for spectrum sensing. Many researches on energy detection are performed on the basis of a precise knowledge of noise power environment to set threshold. In this thesis we use ensemble empirical mode decomposition(EEMD) to decompose signal into licensed users and noise in the noisy environment. The signal power of licensed users and noise power are estimated in the current channel environment. An adaptive threshold is set in an unknown noise environment to improve the performance of energy detector.
中文摘要 I
Abstract II
圖目錄 VI
表目錄 IX
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 研究目的 6
1.4 論文架構 6
第二章 感知無線電介紹 9
2.1 感知無線電背景 9
2.2 感知無線電系統 11
2.3 感知無線電技術 13
2.4 頻譜偵測技術 17
2.4.1 傳輸端偵測(Transmitter Detection) 17
2.4.1.1 匹配濾波偵測器(Matched Filter Detection) 18
2.4.1.2 能量偵測器(Energy Detection) 19
2.4.1.3 循環穩態特徵偵測器(Cyclostationary Feature Detection) 21
2.4.2 合作式偵測(Cooperative Detection) 22
2.4.3 基於干擾的偵測(Interference-based Detection) 25
第三章 希伯特黃轉換介紹 27
3.1 希伯特黃轉換背景介紹 27
3.2 希伯特黃轉換理論介紹 28
3.2.1 希伯特頻譜分析(Hilbert Spectral Analysis, HSA) 29
3.2.2 經驗模態分解法(Empirical Mode Decomposition, EMD) 30
3.2.3 希伯特黃轉換的特性 34
3.2.4 總體經驗模態分解法(Ensemble Empirical Mode Decomposition, EEMD) 36
第四章 使用總體經驗模態分解法之能量偵測器架構 39
4.1 系統簡介 39
4.2 傳統能量偵測器 40
4.3 使用總體經驗模態分解法之能量偵測器演算法 43
4.4 研究步驟 46
第五章 模擬與分析 49
5.1 IMF的模態混雜現象 49
5.2 正弦模擬訊號實例 51
5.3 模擬結果 60
第六章 結論與未來展望 63
6.1 結論 63
6.2 未來展望 63
參考文獻 65
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[20] B. Shent, L. Huang, C. Zhaot, Z. Zhou and K. Kwakt, “Energy Detection Based Spectrum Sensing for Cognitive Radios in Noise of Uncertain Power,” in Proc. International Symposium on Communications and Information Technologies, 2008.
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