臺灣博碩士論文加值系統

在無線通訊系統裡，影響系統性能的主要因素包括：從其他用戶端產生的同頻干擾 (Co-channel Interference, CCI)、符號間干擾 (Inter-symbol Interference, ISI) 和多路徑產生的信號衰落，而盲目訊號分離技術是利用天線陣列來取得發射訊號的空間特徵 (Spatial Signature)，並藉由空間特徵的差異或信號的到達方向 (Direction of Arrival, DOA) 等信號之特性，正確地解析出我們所要的個別訊號源。所以我們可利用盲目訊號分離技術來減少同頻干擾的效應，和抑制多重路徑傳輸所造成的信號衰落現象，因而達成改善系統的性能，增加系統用戶的容量。盲目訊號分離技術可應用於空間分隔多重存取 (Space Division Multiple Access, SDMA) 的範圍，若有成功的盲目訊號分離技術，則我們可利用天線陣列的空間差異 (space diversity)，讓使用者共用相同的參數（如載波或展頻碼），以增加系統用戶的容量和改善通訊品質。
在本篇論文中，我們提出特徵子空間DWILSP演算法，可利用訊號子空間(signal subspace) 技術及相關矩陣的特徵架構，達到提高DWILSP(Decoupled Weighted Iterative Least-square with Projection) 演算法的性能。在DWILSP演算法中，每一使用者之訊號估測與通道辨識是使用直接逆矩陣（direct matrix inversion, DMI） 波束合成，然而，直接逆矩陣波束合成則對於引導向量的誤差十分敏感，會導致訊號消除的效應，因此我們用限制投影（constraint projection）波束合成代替直接逆矩陣波束合成，使得估測待測訊號時的輸出訊號干擾雜訊比（output SINR）被提高，減輕訊號消除的效應。電腦模擬結果顯示特徵子空間DWILSP演算法在共通道 (co-channel) 數位訊號的盲目分離上優於DWILSP。

In wireless communication systems, the principal factors in system performance including the co-channel interference (CCI) from other users, the intersymbol interference (ISI), and the fading caused by multipath transmission. Blind signal separation (BSS) utilizes the antenna arrays to obtain the spatial signatures of transmitting signals. By using the signal properties such as the difference of the spatial signatures and direction of arrivals (DOA’s) of signals, the BSS techniques are able to recover each desired source correctly. So we can use these techniques to reduce the CCI and mitigate the fading phenomenon caused by multipath transmission. Blind signal separation can be applied to space division multiple access (SDMA). We can use the BSS techniques with the space diversity at antenna array to separate users which share the identical parameter like carrier frequency and spreading code. And further, we can increase the system capacity and improve the quality of communication.
The eigenspace-based DWILSP is presented in this thesis, which utilizes the eigenstructure of the correlation matrix to enhance the performance of the decoupled weighted iterative least-square with projection (DWILSP) algorithm. In the DWILSP the signal estimate is interpreted as the direct-matrix-inversion (DMI) beamforming problem. However, the DMI beamformer is sensitive to the steering vector errors, which cause the signal cancellation effects. We then use the constraint projection beamformer instead of the DMI beamformer to alleviate the signal cancellation, where the output signal-to-interference-plus-noise ratio (SINR) is increased during the estimate of signal of interest. Further, to reduce the computational complexity of the developed algorithm, an efficient implementation approach is proposed. Computer simulations are given to demonstrate that the eigenspace-based DWILSP outperforms the DWILSP.

中文摘要 i
ABSTRACT ii
誌謝 iii
CONTENTS iv
LIST OF TABLES vi
LIST OF FIGURES vii
CHAPTER 1. Introduction 1
1.1 Space Division Multiple Access 2
1.2 Blind Signal Separation 3
1.3 Space-Time Processing 4
1.4 Review of Chapter Contents 6
CHAPTER 2. Basic Fundamental of Blind Signal Separation 8
2.1 Data Model 8
2.2 Problem Formulation 10
2.3 Iterative Least-Squares Methods 11
2.3.1 ILSP 12
2.3.2 ILSE 14
2.3.3 DWILSP 16
2.3.4 SIC-ILS 20
CHAPTER 3. Blind Separation of Finite Alphabet Digital Signals Using Constraint Projection Beamforming Techniques 23
3.1 Eigenspace-Based DWILSP 24
3.2 Simplification of Implementation 28
3.3 Consistency and Convergence 31
3.4 RILSP 34
3.5 Simulation Result 36
3.6 Computational Complexity 39
CHAPTER 4. Blind Estimation of FIR-MIMO Channels Using Linear Parameterization 51
4.1 System Description 52
4.2 Blind Identification 60
4.3 Bilinear Approaches 61
4.3.1 Linear Parameterization of the Channel Matrix 61
4.3.2 Second-Order Statistics 66
4.3.3 Algorithm for the Case of Single User and Single Channel 68
4.3.4 Identifiability Conditions 71
4.3.5 Extension to the Case of Multiple Users 73
4.3.6 Extension to the Case of Multiple Channels 74
4.3.7 Derivation of Subspace Method Using the Linear Parameterization 78
4.4 Simulation Procedure 80
4.5 Simulation Results 84
CHAPTER 5. Conclusions 93
REFERENCES 95

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