展頻碼的同步是直接序列分碼多重存取系統中的關鍵技術，因為只有當本地端與接收到之展頻碼正確同步之後，才可能成功完成資料的傳輸。展頻碼同步是由兩個步驟來執行，獲取（粗調）與追蹤（細調），來估測兩個碼之間的延遲之抵補值(延遲差值)。近來，適應性最小均方濾波策略已被提出，以同一個適應性濾波器的架構來執行獲取與追蹤，其中使用最小均方演算法來適應性地調整有限脈衝響應濾波器之權重而尋找延遲之抵補值。一個決策裝置被雇用在適應性最小均方濾波策略中，當成一個決策變數而來指示展頻碼是否同步。因此決策裝置扮演很重要的角色，對於在平均獲取時間之同步效能上。在這篇論文中，只有考慮到展頻碼的獲取。
　　在這論文中，一個新的決策裝置，稱為權重向量範數平方測試法，被設計來搭配適應性最小均方濾波策略，在直接序列分碼多重存取系統中做展頻碼同步。所提出方法之系統機率被推導出來計算平均獲取時間。以偵測機率及平均獲取時間做數值分析與模擬結果，都驗證所提出方法之效能是優越於傳統的均方誤差測試法，尤其在訊號干擾雜訊比相對較低的環境。
　　進一步地，一個有效且聯合調整的展頻碼獲取方法，也就是一個智慧型天線結合所提出的權重向量範數平方測試法之適應性最小均方濾波策略，被設計應用在基地台中，其中在進行展頻碼獲取時所有的天線都會被雇用到。這個新方法是適應性地共同調整展頻碼獲取與智慧型天線之權重係數的一個聯合程序。數值分析與模擬結果顯示所提出方法，在搭配五根天線時的效能比較於搭配單一天線時，在輸出訊號干擾雜訊比，偵測機率與平均同步時間之性能上，大約改善了7分貝。

　　Pseudo-noise (PN) code synchronizer is an essential element of direct-sequence code division multiple access (DS-CDMA) system because data transmission is possible only after the receiver accurately synchronizes the locally generated PN code with the incoming PN code. The code synchronization is processed in two steps, acquisition and tracking, to estimate the delay offset between the two codes. Recently, the adaptive LMS filtering scheme has been proposed for performing both code acquisition and tracking with the identical structure, where the LMS algorithm is used to adjust the FIR filter taps to search for the value of delay-offset adaptively. A decision device is employed in the adaptive LMS filtering scheme as a decision variable to indicate code synchronization, hence it plays an important role for the performance of mean acquisition time (MAT). In this thesis, only code acquisition is considered.
　　In this thesis, a new decision device, referred to as the weight vector square norm (WVSN) test method, is devised associated with the adaptive LMS filtering scheme for code acquisition in DS-CDMA system. The system probabilities of the proposed scheme are derived for evaluating MAT. Numerical analyses and simulation results verify that the performance of the proposed scheme, in terms of detection probability and MAT, is superior to the conventional scheme with mean-squared error (MSE) test method, especially when the signal-to-interference-plus-noise ratio (SINR) is relatively low.
　　Furthermore, an efficient and joint-adaptation code acquisition scheme, i.e., a smart antenna coupled with the proposed adaptive LMS filtering scheme with the WVSN test method, is devised for applying to a base station, where all antenna elements are employed during PN code acquisition. This new scheme is a process of PN code acquisition and the weight coefficients of smart antenna jointly and adaptively. Numerical analyses and simulation results demonstrate that the performance of the proposed scheme with five antenna elements, in terms of the output SINR, the detection probability and the MAT, can be improved by around 7 dB, compared to the one with single antenna case.

Abstract i
Contents ii
List of Figures iv
Chapter 1 Introduction 1
Chapter 2 Adaptive Filtering Scheme with Different Decision Device for Code Acquisition in DS-CDMA Systems
2.1 Introduction 4
2.2 Adaptive LMS Filtering Scheme for Code Acquisition 5
2.2.1 Signal Model 5
2.2.2 Adaptive Acquisition System Description 7
2.2.3 Adaptation Processes 7
2.2.4 Mean and Variance of Optimal Weights 11
2.3 Different Decision Device 13
2.3.1 Proposed Weight Vector Square Norm (WVSN) Test Method 13
2.3.2 Traditional Mean Square Error (MSE) Test Method 14
2.4 Acquisition System Probabilities and Mean Acquisition Time 15
2.4.1 The Proposed WVSN Test Method 15
2.4.2 The Traditional MSE Test Method 16
2.5 Simulations 16
2.5.1 Numerical Analyses and Simulations for PDF of WVSN and MSE Test Methods 16
2.5.2 Numerical Analyses and Simulations for System Probabilities and Mean Acquisition Time of WVSN and Traditional MSE Test Methods 18

Chapter 3 Code Acquisition Using Smart Antennas with Adaptive Filtering Scheme for DS-CDMA Systems
3.1 Introduction 25
3.2 Smart-antennas with Adaptive Filtering Scheme for code Acquisition 26
3.2.1 System Model 26
3.2.2 Joint Adaptation Description 27

3.2.3 Joint Adaptation Processes 27
3.2.4 Mean and Variance of Optimal Weights 32
3.3 System Probabilities and Mean Acquisition Time 33
3.3.1 The Proposed System 33
3.3.2 The Traditional MSE Test Method 34
3.4 Performance of Smart-Antennas 34
3.5 Simulations 34

Chapter 4 Conclusions 48
Appendix A 49
Appendix B 54
Appendix C 55
Appendix D 57
References 65

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