臺灣博碩士論文加值系統

在無線電感知網路系統(cognitive system)中，無線電頻譜感測(spectrum sensing)技術被用來尋找暫時沒被使用的頻譜，提供其他通訊系統有機會來使用，增加頻譜的使用效率。我們知道在數位通訊之中，為了得到通道的資訊，領航信號(pilot signal)扮演必要的角色。實際上，領航信號會不斷地被主要使用者送出，來得到當下的通道資訊，使得接收器能解回原來的信息。所以我們可以利用判斷領航信號的存在與否，來決定主要使用者是否正在使用頻譜。然而，領航信號在經由頻率選擇衰落通道(frequency selective fading)之後，由接收器收到的信號通常具有高相關性，因此被廣泛使用的能量檢測器(energy detector)不是那麼地有效。這篇論文的目的是在基於領航信號的特色之下，來設計較高效能的檢測器，以及討論它們的性能分析。首先，為了得到領航信號的資訊，我們提出幾個領航信號接收器。為了確保頻譜感測在時間上的可靠度，這些接收器被設計的很簡單而且可以即時反應結果。然後，取決於了解領航信號的多寡可分成不同的情況，針對各種情況，我們設計一些檢測器諸如估測關聯器(estimator-correlator)、局部最有效檢測器(LMP detector)以及特徵值檢測器(eigenvalue detector)。我們也放上模擬結果來說明論文中提及的檢測器，以及傳統檢測器的比較結果。為了致使檢測器更實用，在最後，使用隨機矩陣理論(random matrix theory)和高斯值積規則(Gauss quadrature rule)，我們分析各個檢測器的漸進分布函數，以便決定門檻(threshold)而達到所要求的效能。

In cognitive system, spectrum sensing technique exploits unused frequency band for opportunistic wireless transmission, It is known that pilot data transmission is a necessary function in digital communication for channel realization. Then we can determine whether the primary user is on or off by detecting the existence of the pilot signal [1]. However, the received signal is always correlated when it passed through frequency selective fading channel. The widely used energy detector is seems less efficient. In the thesis, we design feature detectors based on pilot data with better performance, and discuss their performance analysis. First, pilot based receivers are proposed in order to provide the pilot information. Then in different cases depending on how we know about the pilot information, some detectors, estimator-correlator, LMP detector and eigenvalue detector, are designed. Simulation results are performed for comparison between proposed detectors and conventional methods. Finally, asymptotical behaviors of these detectors are analyzed by applying random matrix theory and Gauss quadrature rule.

1 Introduction 5
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2 Related Works 9
2.1 Detection Theory . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.1.1 Estimator-Correlator . . . . . . . . . . . . . . . . . . . 10
2.1.2 General Likelihood Ratio Test (GLRT) Detector . . . . 10
2.1.3 Locally Most Power (LMP) Detector . . . . . . . . . . 11
2.2 Random Matrix Theory . . . . . . . . . . . . . . . . . . . . . 12
2.3 Gauss Quadrature Rule . . . . . . . . . . . . . . . . . . . . . . 14
3 Pilot Data in Some Popular Transmission Schemes 17
3.1 Pilot Data in TDMA Scheme . . . . . . . . . . . . . . . . . . 17
3.2 Pilot Data in OFDM Scheme . . . . . . . . . . . . . . . . . . 20
3.3 Sinusoidal Wave . . . . . . . . . . . . . . . . . . . . . . . . . . 22
4 Problem Description and Solutions 23
4.1 Known Channel Gain and Pilot Data . . . . . . . . . . . . . . 24
4.2 Known Pilot Data, but Unknown Channel Gain . . . . . . . . 27
4.3 Only the Period of Pilot Data is Available . . . . . . . . . . . 32
5 Simulation Results 43
6 Conclusion 51
Reference 52

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