傳統上，多數國家對頻譜的分配是採用固定頻帶的處理方式，所以大部分的頻帶都已經被分配到各種通訊服務上面。不過，隨著無線通訊系統技術和設備的快速發展，使用無線系統相關服務的人口也快速增加，所以頻譜的資源變得非常的珍貴，也面臨到不夠使用的問題。因此，許多學者針對如何提高頻譜的使用率，提出了Cognitive Radio的概念。在過去的文獻裡，有討論傳送端傳給接收端的訊號不會受到通道的影響而產生衰落現象，並利用Jarque-Bera常態檢定頻譜感測演算法來判斷是否有閒置的頻帶可使用。但是在現實的環境裡，傳送端到接收端的路徑一定會有衰落現象發生。所以，在我們的研究中，我們考慮了衰落通道的情況。探討在瑞雷通道衰褪環境之後下，對於Jarque-Bera常態檢定頻譜感測演算法會產生什麼樣的效能影響，並且討論了兩種面相的方法來處理接收訊號。一種是我們能否正確地把改變相位的接收訊號解回來，另一種是如何處理複數的接收訊號，是分別考慮實、虛部的接收訊號值，或是拿接收訊號的絕對值去做Jarque-Bera常態檢定頻譜感測演算法。此外，我們也討論其它兩種統計值的檢定方法，並且說明在什麼樣的調變方式和通道環境下，適合什麼樣的檢測方法。

Traditionally, the way to allocate of spectrum sources is fixed band in many countries, and most of the spectrum band are arranged for the certain communication services. However, the technology and devices in the wireless communication system are rapid, and more and more people use the related wireless communication services. Thus, the sources of the spectrum are rare nowadays. People realized that the limitation in the sources and need to enhance the efficient usages ratio, so they discussed the solutions to the problem. Many researches proposed a new concept of the Cognitive Radio network. In the literatures, there is a Jarque-Bera normality test algorithm to detect the spectrum is available or not under the additive white Gaussian noise (AWGN) channel. However, in the realistic world, the fading affects the transmitted signals in the wireless communication. Thus, we consider Rayleigh fading channel mode, and analyze the performance based on JB test for four approaches which depend on two factors. First is that the receiver can recover the random phase or not. Second is that the receiver use the in-phase signal or the absolute values of the signal. Moreover, we compare the performance of JB test with the performance of another statistic test.

中文摘要 ii
ABSTRACT iii
誌謝 iv
CONTENTS v
LIST OF FIGURES viii
LIST OF TABLES xiv
Chapter 1 Introduction 1
Chapter 2 Background Knowledge 3
2.1 Common Spectrum Sensing Method 3
2.2 Jarque-Bera (JB) Statistic Based Detection 4
2.2.1 Skewness 5
2.2.2 Kurtosis 6
Chapter 3 Analysis on JB Statistic Test under AWGN and Rayleigh Fading Channel mode for Different Approaches 7
3.1 System Model 7
3.2 Approach 1 8
3.2.1 Case 1: QPSK 11
3.2.2 Case 2: 16-QAM 14
3.2.3 Case 3: 64-QAM 17
3.3 Approach 2 21
3.3.1 Case 1: QPSK 25
3.3.2 Case 2: 16-QAM 28
3.3.3 Case 3: 64-QAM 31
3.4 Approach 3 34
3.4.1 Case 1: QPSK 35
3.4.2 Case 2: 16-QAM 39
3.4.3 Case 3: 64-QAM 42
3.5 Approach 4 45
3.6 Approaches Analysis 45
Chapter 4 Simulation Results and Discussion 47
4.1 Performance of the JB Statistic Test 47
4.1.1 For Case 1: QPSK 47
4.1.2 For Case 2: 16-QAM 51
4.1.3 For Case 3: 64-QAM 51
4.1.4 Detection Probability with Different SNR 58
4.2 Performance of the Skewness Statistic Test 62
4.2.1 Detection Probability with Different SNR 65
4.3 Performance of the Kurtosis Statistic Test 66
4.3.1 Detection Probability with Different SNR 68
4.4 ROC Curve of Different Statistic Test 71
Chapter 5 Conclusion 82
REFERENCES 83

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