郭斯彥臺灣大學:資訊網路與多媒體研究所何冠錚Ho, Kuan-ChengKuan-ChengHo2010-05-052018-07-052010-05-052018-07-052009U0001-0807200914003200http://ntur.lib.ntu.edu.tw//handle/246246/180680　　無線網路上新的技術持續發展，然而無線網路技術發展受限於頻譜不足的問題，而頻譜是有限且幾乎都已經經由有關當局分配完畢，換言之，頻譜的不足將限制無線網路技術的發展。　研究指出，實際上有在頻繁使用的頻譜只是以分配頻譜其中的少數，因此，為了因應上述問題，頻譜的分配及使用方式變得日益重要。感知無線電(Cognitive Radio, CR)是一個能有效解決此問題的方法，經由感知頻譜使用的情形並且調整自身運作機制，感知無線電能夠偵測出沒有被合法使用者使用的頻段並且使用該頻段，為了能夠做到完全不干擾合法使用者，頻譜偵測被視為感知無線電裡相當關鍵的技術，頻譜感測技術必須達到一定的準確性而且能夠抵禦攻擊者的攻擊。　合作式的頻譜感測(Cooperative Spectrum Sensing, CSS)是一個可以被考慮的頻譜感測架構，在此篇論文裡，將探討使用網格狀(Grid-like)感知無線電架構來增進頻譜感測準確性的可能性，提出以集中式結合地理資訊的頻譜偵測架構，命名為SCSS(Secure Centralized Spectrum Sensing)。　在最後的模擬結果當中，數據顯示SCSS的架構在感測的準確性上有不錯的表現，而當攻擊者在地理位置上有聚集特性時，SCSS的方法能夠有效抵禦攻擊，甚至是攻擊者數量非常多的時候。　　The spectrum bands remain finite while new techniques keep increasing. Today’s wireless networks are regulated by a fixed spectrum assignment policy. Researches show that most of the spectrums are rarely used. The spectrum usage is concentrated on certain portions of the spectrum while a significant amount of the spectrum remains unutilized.　This phenomenon would limit the growing of new technologies in wireless network.　Thus, spectrum utilization seems to become more important. Cognitive Radio (CR) is a revolutionary technology to make use of the spectrum more effectively. In order to avoid the interference to the primary user, spectrum sensing must be sensitive. 　Cooperative Spectrum Sensing (CSS) is one way to increase the reliability of spectrum sensing. The information fusion technique is a key component of CSS. In this paper, we adopt a grid-like model for CR networks, and we utilize geographical information to propose a new two-level fusion scheme called Secure Centralized Spectrum Sensing (SCSS). The simulation results show when the attackers present high density aggregation at some area, the correct sensing ratio of SCSS increases even when the number of attackers is very large.Chapter 1 Introduction 1.1 Introduction of NeXt Generation Network 1.2 Cognitive Radio 3.2.1 Characteristics of Cognitive Radio 4.2.2 Three Researches about Cognitive Radio 6hapter 2 Spectrum Sensing 9.1 Introduction of Spectrum Sensing 9.1.1 Energy detection 10.1.2 Matched filter detection 11.1.3 Cyclostationary feature detection 12.2 Threats to Spectrum Sensing 13.3 Threats to Spectrum Sensing 15.3.1 Centralized Cooperative Spectrum Sensing 15.3.2 Distributed Cooperative Spectrum Sensing 16.4 Threats to Spectrum Sensing 16.4.1 Incumbent Emulation (IE) attacks 17.4.2 Spectrum Sensing Data Falsification (SSDF) attacks 17hapter 3 Related Works 18.1 Classical Data Fusion Techniques 18.1.1 Decision fusion 18.1.2 Bayesian detection 19.1.3 Neyman-Pearson test 20.2 Weighted Sequential Probability Ratio Test 21.3 Cooperative Spectrum Sensing Based on SNR Comparison in Fusion Center for Cognitive Radio 24hapter 4 Secure Centralized Spectrum Sensing (SCSS) 27.1 System model of SCSS 27.2 Procedures of SCSS 29.2.1 Geographical Weight 31.2.2 Reputational Weight 33.3 Priori Probabilities of SCSS 34hapter 5 Simulations and Results 37.1 Simulation Environment 37.2 Simulation Results 40.2.1 Impact by different numbers of attacker 40.2.2 Different aggregation behaviors of attackers 45hapter 6 Conclusions 47hapter 7 References 48application/pdf1276991 bytesapplication/pdfen-US感知無線電頻譜偵測Cognitive RadioSpectrum Sensingthesishttp://ntur.lib.ntu.edu.tw/bitstream/246246/180680/1/ntu-98-R96944017-1.pdf