臺灣博碩士論文加值系統

摘要
感知無線電網路藉由動態頻譜存取而擁有更高的頻譜效率。因此，它將會成為未來無線通訊系統最常使用來減輕頻譜缺乏問題的技術。在感知無線電網路中頻譜偵測是主要且棘手的任務。然而，由於遮蔽、干擾、和無線通道的時變性質的影響，造成各別的感知無線電無法可靠和迅速地檢測出主要訊號是否存在、在本論文中，我們提出一簡單但有效率的合作式頻譜偵測且基於能量檢測。我們在次要的使用者與聯合中心之間考慮了兩個案例。一是只考慮通道雜訊，另外是考慮通道雜訊和干擾。最後，我們對修改的反射係數作最佳化，來找出最佳線性組合係數。藉由電腦模擬，我們觀察到所提出的合作式方法有較好的成果，而且藉由增加次要使用者的數目來改善偵測可靠性。

Abstract
Cognitive radio network enables much higher spectrum efficiency by dynamic spectrum access. Therefore, it will be a popular technique for future wireless communications to mitigate the spectrum scarcity issue. Spectrum sensing is a main and tough task in cognitive radio networks. However, due to the effect of shadowing, fading, and time-varying nature of wireless channels, the individual cognitive radio may not be able to reliably and quickly detect the existence of a primary signal. In this thesis, we propose a simple yet efficient cooperation spectrum sensing based on energy detection, and consider the channel between the secondary user and fusion center in two cases. First, we consider only the channel noise between the secondary user and the fusion center (i.e., constant AWGN channel), and then we extend to consider both the perturbation noise and channel fading between the secondary user and the fusion center (i.e., fading channel). Our objective is to improve the detection performance while considering a realistic system environment. Finally, we optimize a modified deflection coefficient to find the optimal linear combining weights. From the simulations, we can observe that the proposed cooperation method has the better detection performance than the other methods, and the sensing reliability improves as the number of secondary users increase.

Chapter 1 Introduction 1
1.1 Significance 1
1.2 Motivation 2
1.3 Contribution 3
Chapter 2 Background Review 5
2.1 Cognitive Radio Networks 5
2.1.1 Introduction to Cognitive Radio 5
2.1.2 Cognitive Task 8
2.1.3 Historical Notes 10
2.2 Statistical Decision Theory 11
2.3 Energy Detection 18
2.3.1 Introduction to Energy Detection 18
2.3.2 Energy Detection in White Noise 19
2.4 SNR Wall Reduction 24
Chapter 3 Distributed Cooperative Sepectrum
Sensing for Two Cases 27
3.1 System Model 27
3.2 Cooperative Spectrum Sensing 29
3.2.1 Local Sensing 30
3.2.2 Global Detection 33
I. Constant AWGN Channel between Secondary
User and Fusion Center 33
II. Fading Channel between Secondary User
and Fusion Center 37
3.3 Performance Optimization 42
3.4 Simulation Result 46
Chapter 4 Conclusion 54

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