臺灣博碩士論文加值系統

感知無線電(Cognitive radio, CR)是新的智慧型無線通訊技術，用於解決頻譜擁塞和頻帶未妥善利用的問題。感知無線電中，動態頻譜管理(dynamic spectrum management)為其主要的機制，但在這之前，要先在無線環境中去偵測和辨識訊號是否存在，也就是頻譜偵測的技術。
能量偵測器(Energy detector, ED)即為頻譜偵測的其中一項技術，由於複雜度低且只需要較少的預先知識，因而被廣泛應用在感知無線電系統中，但是缺點為在低訊號雜訊比(Signal to Noise Ratio, SNR)的環境下偵測效能並不好。
在本篇論文中，以韋爾奇週期圖法在頻域下去分析，並分段去除雜訊來改善能量偵測器的效能。根據模擬結果，在此架構下比傳統能量偵測器以及週期圖法能量偵測器的頻譜偵測效能好，進而達到改善偵測效能的目的。

Cognitive radio(CR) is a novel smart wireless communication technology which solves the problems of the spectrum congestion and under-utilization. In cognitive radio networks, dynamic spectrum management is the most important mechanism of cognitive radio, but before implementing operational dynamic spectrum management, first it has to detect a wireless environment and determine the existence of signal which is spectrum sensing technique.
Energy detector is one of spectrum sensing technique approach which is the most widely used in cognitive radio system because of the low complexity and the requirement of less prior knowledge. But the disadvantage is that the detection performance is bad in environments with low signal to noise ratio.
In this thesis, Welch’s Periodogram-Based Algorithm is used at frequency domain and segmenting bandwidth to denoise for improving the energy performance of the detector. The simulation results show that the architecture proposed provides better spectrum sensing performance than the traditional energy detector and traditional Welch’s periodogram-based energy detector do, thus achieving the goal of improvement.

中文摘要 I
Abstract II
誌謝 III
目錄 IV
圖目錄 VI
表目錄 VIII
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 3
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 14
2.4 頻譜偵測簡介 16
2.4.1 傳輸端偵測 17
2.4.2 能量偵測器 17
2.4.3 匹配濾波偵測器 19
2.4.4 循環穩態特徵偵測器 21
第三章 頻域下的週期圖法理論應用分析 23
3.1 週期圖法簡介 23
3.2 韋爾奇週期圖法應用於能量偵測器 26
第四章 頻域分段去除雜訊演算法之韋爾奇週期圖法-能量偵測器 35
4.1 系統架構 35
4.2 頻域分段去除雜訊演算法 38
4.2.1 設計想法 38
4.2.2 演算法參數 40
4.3 新的偵測模型 41
第五章 模擬與分析 45
5.1 偵測器效能評估 45
5.1.1 接收操作特性曲線模擬與分析 45
5.1.2 不同平均訊號雜訊比對於偵測機率模擬和分析 48
第六章 結論與未來展望 53
6.1 結論 53
6.2 未來展望 53
參考文獻 55

[1] M. A. McHenry, “NSF spectrum occupancy measurements project summary,” Shared Spectrum Company, August 2005.
[2] Federal Communications Commission, “Notice of proposed rule making and order: Facilitating opportunities for flexible, efficient, and reliable spectrum use employing cognitive radio technologies,” ET Docket No. 03-108, February 2005.
[3] L. Khalid, and A. Anpalagan, “Emerging cognitive radio technology: Principles, challenges and opportunities,” Computers & Electrical Engineering, vol. 36, no. 2, pp. 358-366, March 2010.
[4] S. Haykin, “Cognitive radio: brain-empowered wireless communications,” IEEE Journal on Selected Areas in Communications, vol. 23, no. 2, pp. 201-220, February 2005.
[5] Weifang Wang, “Spectrum Sensing for Cognitive Radio,” The third International Symposium on Intelligent Information Technology Applications Workshop (IITAW), November 2009.
[6] 林鴻宗, “感知無線電下以小波為基礎之去除雜訊演算法之可適應性能量偵測器,” 國立東華大學電機工程學系碩士論文, 民國100年7月。
[7] J. Mitola, and G. Q. Maguire Jr., “Cognitive radio: making software radios more personal,” IEEE Personal Communications, vol. 6, no. 4, pp. 13-18, August 1999.
[8] Wei Zhang, R. K. Mallik, and K. Letaief, “Cooperative Spectrum Sensing Optimization in Cognitive Radio Networks,” IEEE International Conference on Communications 2008 (ICC '08), pp. 3411-3415, May 2008.
[9] I. Budiarjo, M.K. Lakshmanan, and H. Nikookar, “Cognitive Radio Dynamic Access Techniques,” Springer US Journal on Wireless Personal Communications, vol. 45, no. 3, pp. 293-324, February 2008.
[10] http://www.cteccb.org.tw/pdf/IECQ-52-5.pdf.
[11] N.A. Moseley, “Radio resource discovery for ad-hoc wireless networking,” Electrical Engineering, Mathematics and Computer Science, August 2004.
[12] T. Weiss, J. Hillenbrand, A. Krohn, and F. K. Jondral, “Mutual interference in OFDM-based spectrum pooling systems,” IEEE 59th Vehicular Technology Conference 2004 (VTC '04), vol. 4, pp. 1873-1877, May 2004.
[13] T. Weiss, and F. K. Jondral, “Spectrum pooling: an innovative strategy for the enhancement of spectrum efficiency,” IEEE Communications Magazine, vol. 42, no. 3, pp. S8-14, March 2004.
[14] T. Yucek, and H. Arslan, “A survey of spectrum sensing algorithms for cognitive radio applications,” IEEE Communications Surveys & Tutorials, vol. 11, no. 1, pp. 116-130, March 2009.
[15] I. F. Akyildiz et al., “Next Generation/Dynamic Spectrum Access/Cognitive Radio Wireless Networks: A Survey,” Computer Networks Journal, vol. 50, pp. 2127-2159, September 2006.
[16] A. Shahzad et al., “Comparative Analysis of Primary Transmitter Detection Based Spectrum Sensing Techniques in Cognitive Radio Systems,’’ Australian Journal of Basic and Applied Sciences, vol. 4, no. 9, pp. 4522-4531, January 2010.
[17] E. Hossain, V. Bhargava, Cognitive Wireless Communication Networks, Springer, 2007.
[18] L. Doyle, Essentials of Cognitive Radio, Cambridge University Press, 2009.
[19] D. Cabric, A. Tkachenko, and R.W. Brodersen, “Spectrum Sensing Measurements of Pilot, Energy, and Collaborative Detection,’’ IEEE Military Communications Conference 2006 (MILCOM '06), pp. 1-7, October 2006.
[20] M. Subhedar, and G. Birajdar, “Spectrum sensing techniques in cognitive radio networks: A survey,’’International Journal of Next-Generation Networks (IJNGN), vol. 3, no. 2, June 2011.
[21] I. F. Akyildiz, B. F. Lo, Ravikumar, and R. Balakrishnan, “Cooperative spectrum sensing in cognitive radio networks: A survey,” Physical Communication, pp. 40-62, March 2011.
[22] A. Tkachenko, D. Cabric, and R. W. Brodersen, “Cyclostationary feature detector experiments using reconfigurable BEE2,” IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks 2007 (DySPAN '07), pp. 216-219, April 2007.
[23] K. Xu, and G. Hu, “Signal Analysis and Processing,” 清華大學出版社, 2006.
[24] D. M. Martínez, and A. G. Andrade, “Performance evaluation of Welch's periodogram-based energy detection for spectrum sensing,” IET Communications, vol. 7, no. 11, pp. 1117-1125, July 2013.
[25] S. M. Kay, Fundamentals of statistical signal processing: detection theory, Prentice-Hall, 1998.
[26] P. Welch, “The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms,” IEEE Transactions on Audio and Electroacoustics, vol. 15, no. 2, pp. 70-73, June 1967.
[27] D. Cabric, A. Tkachenko, and R. W. Brodersen, “Experimental study of spectrum sensing based on energy detection and network cooperation,” The first international workshop on Technology and policy for accessing spectrum (TAPAS '06), Article no. 12, June 2006.
[28] S. J. Shellhammer, N. Shankar, R. Tandra, and J. Tomcik, “Performance of power detector sensors of DTV signals in IEEE 802.22 WRANs,” The first international workshop on Technology and policy for accessing spectrum (TAPAS '06), Article no. 4, June 2006.