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研究生:張鈞傑
研究生(外文):Chun-Chieh Chang
論文名稱:感知無線電下低複雜度之循環週期頻譜偵測設計
論文名稱(外文):A Low-complexity Design of Cyclostationary Spectrum Sensing in Cognitive Radio
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:2011
畢業學年度:99
語文別:中文
論文頁數:59
中文關鍵詞:感知無線電循環週期頻譜特性偵測低複雜度
外文關鍵詞:Cognitive RadioCyclostationary Feature Detectionlow complexity
相關次數:
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  • 下載下載:43
  • 收藏至我的研究室書目清單書目收藏:0
近年來,由於通訊產業的快速發展,而導致頻譜的缺乏。因此感知無線電就被提出,以用來有效的解決頻譜資源缺乏的情況。感知無線電主要的功用就是讓次要使用者可以去有效的利用主要使用者未使用的頻段,而卻不會干擾到主要使用者的一種技術,以達到最有效率的使用頻帶。在本篇論文中,我們使用眾多頻譜偵測方法中的一種,循環週期頻譜(Cyclostationary)特性偵測方法,此種方法是利用循環週期頻譜偵測的特性,來對接收到的信號去偵測目標使用者。循環週期頻譜(Cyclostationary)特性偵測不僅僅可以區分來自目標使用者的干擾,以及不同的調變系統。然而,循環週期頻譜偵測方法卻有著高計算複雜度的缺點。因此,本篇論文提出一種經由小波轉換(Wavelet Transform),而產生具有低複雜度的循環週期頻譜偵測方法。
In recent year, the development of wireless communication systems results in the lack of radio spectrums. Therefore, cognitive radio (CR) networks are proposed to use the spectrum more efficiently .In order to achieve this, CR networks may identify the primary user and the secondary user employs the unoccupied spectrum without interference to the primary user. In this paper, we use one of the spectrum sensing techniques, cyclostationary feature detection, to detect the target users by utilizing the cyclostationary feature of the observed signals. Cyclostationary feature detection distinguishes not only interference from the target users but also different modulation schemes and users. However, the computational complexity is very high for the cyclostationary feature detection. This paper proposes a new low-complexity cyclostationary feature detection by using Wavelet Transform.
中文摘要 .................................................I
ABSTRACT..................................................II
誌謝.....................................................III
目錄.......................................................V
圖目錄..................................................VIII
表目錄....................................................XI
第一章 緒論...............................................1
1.1研究動機與目的..........................................1
1.2研究方法與步驟..........................................3
1.3論文架構................................................4
第二章 感知無線電介紹.....................................5
2.1感知無線電簡介..........................................5
2.2感知無線電所使用的頻譜偵測方法.........................10
第三章 二維小波轉換以及傅立葉轉換複雜度介紹..............19
3.1二維小波轉換介紹.......................................19
3.2傅立葉轉換複雜度介紹...................................23
第四章 循環週期頻譜偵測加上二維小波轉換所提出的架構......31
4.1系統架構...............................................31
4.2複雜度的計算介紹.......................................34
4.3複雜度的計算比較.......................................36
4.4公式推導...............................................38
第五章 模擬與分析........................................41
5.1參數設定...............................................41
5.2二位元相位偏移調變(BPSK)以及四位元相位偏移調變(QPSK)....................................................42
5.3調幅(AM)以及正交振幅調變(QAM)..........................46
5.4頻率 與循環頻率 之ROC曲線圖............................50
5.5複雜度比較與計算.......................................54
第六章 結論與未來展望....................................55
6.1結論...................................................55
6.2未來展望...............................................55
參考文獻..................................................57
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[2] Z. Ji and K.J.R. Liu ,“Cognitive Radios For Dynamic Spectrum Access - Dynamic Spectrum Sharing: A Game Theoretical Overview,” IEEE Communications Magazine, vol. 45, issue. 5, pp. 88-94, 2007.
[3] E.L. da Costa, Detection and Identification of Cyclostationary Signals, Master’s dissertation, Naval postgraduate school, Monterey CA, 1996.
[4] A.F Molisch and L.J Greenstein and M. Shafi ,“Propagation Issues for Cognitive Radio,” Proc. IEEE, vol. 97, issue. 5, pp. 787-804, 2009.
[5] I. Budiarjo , M.K Lakshmanan and H. Nikookar ,“Cognitive Radio Dynamic Access Techniques,” Wireless Personal Communications, vol. 45, no. 3, pp. 293-324, 2007.
[6] S. Hayjin ,“Cognitive Radio: Brain-Empowered Wireless Communications,” IEEE J. Sel. Areas Commun., vol. 23, no. 2, pp. 201–220, Feb. 2005.
[7] B. Wang and K.J.R. Liu ,“Advances in Cognitive Radio Networks: A Survey,” IEEE J. Sel. Topics Signal Process., vol. 5, no. 1, pp. 5–23, Feb. 2011.
[8] http://www.cteccb.org.tw/pdf/IECQ-52-5.pdf
[9] K.B Letaief and W. Zhang ,“Cooperative Communications for Cognitive Radio Networks,” Proc. IEEE , vol. 97, issue. 5, pp. 878-893, 2009.
[10] D. Cabric , A. Tkachenko and W. Brodersen ,“Spectrum Sensing Measurements of Pilot, Energy, and Collaborative Detection,” in Proc. MILCOM , Oct. 2006, pp. 1–7.
[11] Q. Wu, F. A. Merchant, K. R. Castleman, Wavelet Image Processing, Academic Press, pp.79-111, 2008.
[12] M.D Shieh , “An Efficient FFT/IFFT Compiler For Different Applications,” Master Thesis , National Cheng Kung University , 2006.
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[15] P.D. Sutton, K.E. Nolan, Linda E. Doyle. “Cyclostationary Signatures in Practical Cognitive Radio Applications,” IEEE J. Sel. Areas Commun., vol. 26, pp. 13–24, Jan. 2008.
[16] T. Yucek and H. Arslan, “A Survey of Spectrum Sensing Algorithms for Cognitive Radio Applications,” IEEE Commun. Survey & Tutorials, vol. 11, no. 1, pp. 116-130, 2009.
[17] S. Haykin, D. J. Thomsom, and J. H. Reed, “Spectrum Sensing for cognitive radio,” Proc. IEEE, vol. 97, no.5, pp. 849–877, May, 2009.
[18] I. J. Mitola and G. Q. Maguire, “Cognitive radio: making software radios more personal,” IEEE Pers. Commun., vol. 6, no. 4, pp. 13–18, Aug. 1999.
[19] S. Haykin, “Fundamental issues in cognitive radio,” in Cognitive Wireless Communication Networks, E. Hossain and V. K. Bhargava, Eds. New York: Springer, pp. 1–43, 2007.
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