臺灣博碩士論文加值系統

本論文主要是對第三代寬頻分碼多工通訊系統中的通道估測部分做整體的介紹。在同調寬頻分碼多工上鏈通訊系統中，週期性出現於控制通道中的導引信號被用來做通道估測。快速的傳輸功率控制及手機的省電在寬頻分碼多工上鏈通訊系統中是非常重要的課題。我們提出了移動平均的方法及滑動窗型平均的方法來減小控制訊號決定時間的延遲。根據控制通道及資料通道的資訊，我們得出一個低複雜度的通道估測方法來減小控制通道傳輸能量。經過整篇論文的探討及模擬，我們發現利用滑動窗型平均法六並根據控制通道及資料通道的資訊，在每個位元的錯誤率或延遲時間及功率節省上整體的表現可以得到較其他方法好的結果。

In this thesis, the channel estimation methods for next generation wideband code-division multiple-access (WCDMA) system were studied. Coherent WCDMA reverse link periodically transmitting pilot symbols in front of every control channel slot to estimate the fading channel is considered. For the WCDMA reverse link, fast transmit power control (TPC) and power saving of mobile station are the important issues. We proposed moving average method and sliding window methods to overcome the decision delay of the control symbols. We also proposed a low-complexity channel estimation method based on both control and data channels to reduce the required power of a control channel. After investigation and simulation in this thesis, we conclude that the sliding window type-VI channel estimation based on both control and data channels is better than other methods in the overall performance of BER, delay time and power reduction.

Contents
摘要I
ABSTRACTII
誌謝III
CONTENTSIV
LIST OF TABLESVIII
LIST OF FIGURESIX
CHAPTER 1 INTRODUCTION1
1.1 WIDEBAND CODE DIVISION MULTIPLE ACCESS TECHNIQUES1
1.2 CONCEPT OF CHANNEL ESTIMATION AND RAKE RECEIVER2
1.3 ORGANIZATION OF THE THESIS4
CHAPTER 2 SYSTEM STRUCTURE5
2.1 TRANSMITTER5
2.1.1 Frame Structure5
2.1.2 Spreading and Modulation6
2.2 CHANNEL MODEL10
2.2.1 Jakes Model10
2.2.2 Doppler effect11
2.2.3 Tapped-Delay-Line Model12
2.3 STRUCTURE OF RECEIVER13
2.3.1 Receiver Structure13
2.3.2 Equivalent Receiver Structure14
2.4 SYSTEM MODEL16
2.4.1 Transmitter16
2.4.2 Channel16
2.4.3 Receiver17
CHAPTER 3 CHANNEL ESTIMATION BASED ON CONTROL CHANNEL20
3.1 INTRODUCTION20
3.2 LINEAR INTERPOLATION22
3.2.1 Linear Interpolation Method22
3.2.2 Simulation Results23
3.3 LEAST-SQUARE ALGORITHM25
3.3.1 Introduction25
3.3.2 Simulation Results26
3.4 MAXIMUM LIKELIHOOD ESTIMATION28
3.5 MOVING AVERAGE29
3.5.1 Introduction29
3.5.2 Simulation Results30
3.6 SLIDING WINDOW33
3.6.1 Sliding Window types33
3.6.2 Sliding Window Based on Previous Data34
3.6.3 Sliding Window Based on Previous Data and Future Data35
3.6.4 Simulation Results36
3.7 ANALYTICAL AND SIMULATION RESULTS40
3.7.1 BER Performance40
3.7.2 Delay Time44
3.7.3 Computational Complexity45
CHAPTER 4 CHANNEL ESTIMATION BASED ON BOTH CONTROL AND DATA CHANNELS47
4.1 INTRODUCTION47
4.2 OPTIMAL CHANNEL ESTIMATION BASED ON BOTH CONTROL AND DATA CHANNELS48
4.3 THE LOW COMPLEXITY CHANNEL ESTIMATION ALGORITHM BASED ON BOTH CONTROL AND DATA CHANNELS52
4.4 ANALYTICAL AND SIMULATION RESULTS53
4.4.1 BER Performance53
4.4.2 Delay Time62
4.4.3 Computational Complexity63
CHAPTER 5 CONCLUSIONS64
REFERENCES66
自傳69

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