在傳統盲蔽式接收器中，針對通道參數做估測時，相位模糊 (Phase ambiguity)問題通常都被忽略，亦即大多數文獻都以實際通道的第ㄧ個通道參數 (Channel coefficients) 當作估測器 (Estimator) 的初始值來執行通道估測，實際上是不切實際的。在Chern與Huang所發表的文獻中，他們結合時間-空間編碼 (ST-BC)與非補零 (None zero-padding)冗餘符碼的概念，設計出一組混合式展頻碼 (Hybrid padding chip-spreading codes)序列，以解決相位模糊的問題。在設計上，於接收器端將混合式展頻碼序列移除，亦可有效抑制符碼區塊間干擾 (Inter-block interference; IBI)。同時利用傳統所謂的最小/最大化(Min/max )法則設計最接接收器，可以進一步將多用戶干擾 (MAI) 的效應降低。實際上最小/最大化法是一種兩階段將接收濾波器組(Filter bank)做最佳化的方法；亦即於第一階段先將所收到經由具兩個分支(Two branches)之濾波器組的輸出訊號的功率最小化，有別於傳統根據線性限制最小變異數(Linearly constrained minimum variance ; LCMV)法則，針對濾波器組的權重係數作最小化，本論文則是採用Chern與Huang於他們的文獻中所提出的線性限制常模(Linearly constrained constant modulus ; LCCM)法。之後，於第二階段再將所求得最佳濾波器組(Optimal filter bank)的輸出訊號所組成之自變異矩陣(Auto-covariance matrix)，求取最小特徵值(Eigen-value)所對應之特徵向量(Eigen-vector)用以估測通道之脈衝響應 (Channel impulse response; CIR)。在本論文我門針對他們的作法提出新的修正法，利用此混合式展頻碼序列所估測出的初始通道估測值，配合新的簡化型適應性功率法 (Simplified Adaptive power method)，完成通道估測。這個新方法也可以有效對治因為不匹配效應 (Mismatch effect)，所導致的系統效能之降低等問題。傳統文獻中的功率法則 (Power method) 找尋特徵向量對應之最大特徵值來完成估測通道，此方法計算複雜度約為O(n2)。在本論文我們擬提出的改進方法即，使用簡化型適應性功率法則來降低運算複雜度，此方法會在找尋特徵向量對應之最大特徵值時複雜度約降為O(4n)，相較於傳統的功率法則複雜度O(n2)將會減少很多，尤其是在多輸入-多輸出的分碼多工系統中。為了確實符合在通道參數估測過程中之限制條件(Unit norm constraint)以求取最小特徵向量(Eigen-vector)，我們也針對接收訊號的接收模式做修正，了通道參數，使限制性條件變得更準確，讓系統效能更為提昇。

In this thesis, we consider the space-time block coding (ST-BC) MIMO-CDMA transceiver framework, associated with the hybrid non-zero padding assisted chip-spreading codes in the transmitter. The MIMO-CDMA system considered here is an extension of the works proposed by Chern and Hunag. The phase ambiguity is known to be one of the primary problems in conventional blind receiver design; usually it is ignored by assuming that the initial value of channel estimator is available. Unfortunately, this assumption is not true in practical applications since the true channel state information is not available in the receiver, and the scaling process is impractical. As described earlier, in the works proposed by Chern and Huang, they proposed a new hybrid non-zero-padding chip-spreading code sequences for the corresponding transmit-antennas to resolve the phase ambiguity problem. Also, in the receiver, by removing these coded sequences, the effect of inter-block interference (IBI) could be partially alleviated. Furthermore, the Min/Max criterion based linearly constrained constant modulus (LCCM) algorithm is proposed for optimal two-branch filter- bank design and implemented with the adaptive constrained RLS algorithm with the generalized side-lobe canceller (GSC) framework. It is basically a two-steps optimal receiver design. In the first step, the output power of the filter-bank  receiver us minimized, while in the second step, the optimization procedure is performed to find the maximized the eigenvalue of the corresponding output covariance matrix of the filtered output vector, obtained after the first-step, yields the channel vector estimation results. In this thesis, we propose a new approach by using the natural power method instead of the conventional power method associated with the initial channel estimation results obtained by the proposed hybrid non-zero-padding chip-spreading code sequences to perform the semi-blind channel estimation. Associated with the modification of the received signal model to assure the unit norm constraint, to complete the channel estimation and to achieve better bit error rate (BER) performance. The complexity of using the natural power method is O(4n),that is much less compared to the complexity of conventional power method, it required O(n) for estimating the channel vector.

TABLE OF CONTENTS
CHAPTER 1 INTRODUCTION	1
CHAPTER 2 CONVENTIONAL BLIND CAPON RECEIVER ST-BC MIMO-CDMA SYSTEM	6
2.1 Introduction	6
2.2 Review of Space-Time Block Code (ST-BC)	6
2.3 Downlink Space-Time Block Coded MIMO-CDMA in Frequency-Flat Channels	9
2.4Phase Ambiguity associated with Blind ChannelEstimation	15
CHAPTER 3 Modified Adaptive Semi-Blind ST-BC 
MIMO-CDMA Receiver Design	16
3.1 Introduction	16
3.2 Hybrid Non-zero Pre-coded ST-BC MIMO-CDMA Receiver in Multipath Channels	17
3.3Two-Branches Filter Banks Permutation in the CDMA Systems	30
3.4MIMO CM-GSC-RLS Algorithm with Capon Channel Estimation	32
3.5 Natural Power Method to Find the Dominant Eigenvalue	37
3.5.1Method to Resolve the Problem of Phase Ambiguity  40
3.6 System Model in Time-Varying Channels	41
CHAPTER 4 SIMULATION RESULTS	44
4.1 Preliminaries	44
4.2 Simulation Results and Some Observations	45
CHAPTER 5 CONCLUSIONS	54
Appendix A Method of Selecting   in MIMO CM-GSC-RLS Algorithm	56 MMSE Receiver and Blind Capon Receiver	58

References	62
 
LIST OF FIGURES
Fig.2.1 System configuration of two-branch MRRC.	8
Fig.2.2 System configuration of ST-BC.	8
Fig.2.3 Configuration of MIMO-CDMA systems with ST-BC	10
Fig.3.1CDMA systems with hybrid non-zero pre-coded (the kth user)	19
Fig.3.2Hybrid non-zero pre-coded MIMO-CDMA Receiver in multipath channel	21
Fig 3.3Illustration of ISI in multipath channels in time slot 2t−1 (Transmitted Symbol	24
from Tx1)	24
Fig 3.4Illustration of ISI in multipath channels in time slot 2t (Transmitted Symbol	24
from Tx1)	24
Fig 3.5Illustration of ISI in multipath channels in time slot 2t+1 (Transmitted Symbol	25
from Tx2)	25
Fig 3.6Illustration of ISI in multipath channels in time slot 2t+2 (Transmitted Symbol	25
from Tx2)	25
Fig.3.7Configuration of the MIMO CM-GSC-RLS algorithm	34
Fig.4.1SINR comparison of different receivers without mismatch effect, L=3, N=1	47
Fig.4.2BER comparison of different receivers without mismatch effect, L=3, N=1	48
Fig.4.3BER comparison of different receivers without mismatch effect, L=3, N=2	48
Fig.4.4SINR comparison of different receivers with mismatch effect, L=3, N=1	49
Fig.4.5BER comparison of different receivers with mismatch effect, L=3, N=1	50
Fig.4.6BER comparison of different receivers with mismatch effect, L=3, N=2	50
Fig.4.7BER of different receivers without mismatch effect in time-varying channels, L=3, N=1	52
Fig.4.8BER of different receivers with mismatch effect in time-varying channels, L=3, N=1	52
Fig.4.9Tracking ability of  	53
LIST OF TABLES
Table 3.1 Transmitted symbols in six time intervals and different Tx	19
Table 3.2 Summary of the CM-GSC-RLS Algorithm with Method of Selecting   for MIMO-CDMA receiver	36
Table 3.3 Transmitted symbols and corresponding channels in time-varying case	43

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