為因應未來無線通訊快速成長的需求，系統提供高資料傳輸的能力與高容量之多用戶傳輸服務是必須的，基於此種考量，一種新型的數位調變多重進接技術因應而生：亦即結合了正交分頻多工與分碼多重進接技術的多載波分碼多重進接技術。它允許個別用戶之展頻碼獨立調變於各個載波之上，使通道衰減顯現平坦之特性，並提供頻率多樣之效益，進而可運用簡單之等化器達成對抗符際干擾之問題。然而，多路徑通道效應造成展頻碼失去正交性，衍生出多用戶干擾而使得系統容量下降；除此之外，其他系統的窄頻干擾亦有其負面影響，多使用者的干擾是限制這項能力的主因之一，因此，有效的干擾抑制法便成為提昇系統效能不可或缺的技術。本論文中提出對角線載入限制性遞迴最小均方值演算法，此演算法的作法是對接收訊號的自相關矩陣的對角線元素，減去干擾加雜訊的能量，然後再按照傳統限制性遞迴最小均方值演算法的方法所推導出來。因為我們的演算法在每一次遞迴疊代求自相關矩陣的時候，都會在其對角線上的各個元素上減去該時刻所估測出來的干擾能量，而且理論上各使用者的傳送信號應該相關性佷低，故當我們對自相關矩陣的對角元素做減掉干擾的動作時，大部分的干擾會先被事先消除，所以相較於單純限制性遞迴最小均方值演算法而言，我們的演算法在某一些情況之下可以獲得更佳的效果。最後，藉由模擬分析，證實所提出之DL-LC-RLS演算法可有效對抗多使用者干擾，提昇系統效能,而且相較於單純只有加入限制性條件的演算法具有更佳的效能。

There are many novel techniques have been invented to provide high-data rate with high quality communication services for future wireless communications systems. Recently, a novel digital modulation technology for multiple accesses, referred to as the Multi-Carrier Code Division Multiple Access (MC-CDMA), has been proposed to support high data rate transmission; it is based on the combination of CDMA and orthogonal frequency division multiplexing (OFDM). The MC-CDMA has been shown to be an effective technique for combating multipath fading. With MC-CDMA system, a user’s spreading code can be modulated on separate subcarriers, undergo frequency-flat fading channel and offers frequency diversity advantage. But in a multi-user environment, othogonality among spreading codes is severely distorted due to multipath delay spread, such that the system capacity will be limited by the multiple access interferences (MAI). Similar situations exist due to possible narrowband interference (NBI) from other systems. Effective interference reduction will render system capacity to increase, which means interference suppression techniques are vital in improving overall system performance. In this thesis, we propose a new linearly constrained recursive least square algorithm, with diagonal loading approach, referred to as the DL-LC RLS algorithms, to further improve the system performance. The proposed diagonal loading RLS algorithm is different from conventional diagonal loading RLS algorithm, in which the diagonal loading was used to improve the robustness to pointing errors in beamforming problem. However, in this thesis, the diagonal loading approach could be used to alleviate the effect due to multiple access interference (MAI), such that under certain circumstances, better performance could be achieved. Basically, in the proposed algorithm, the power of interference plus noise of received signal will be estimated and subtracted from the diagonal terms of the autocorrelation matrix of received signal. After that instead of using the original autocorrelation matrix, the new correlation matrix, with subtracting power related to the interference plus noise, will be involved during the adaptation processes for updating the weights of the multi-user detector. Finally, computer simulation results, in terms of bit error rate, are used to demonstrate the merits of the proposed scheme compared with the conventional RLS algorithm approach without using the diagonal approaches.

Contents
Acknowledgement i
Abstract ii
Contents iii
List of Figures and Tables v
Chapter 1 Introduction 1
Chapter 2 The Multi-User Multi-Carrier CDMA Systems with Conventional Linearly Constrained Detector
2.1 Introduction 5
2.2 Overview of Multi-User Multi-Carrier CDMA Systems 6
2.2.1 Transceiver Structure of The MC-CDMA Systems 8
2.2.2 Model Description of The MC-CDMA Systems 10
2.3 Conventional Multi-User Detectors for MC-CDMA Systems 16
2.3.1 Maximum Ratio Combining 18
2.3.2 Conventional PLIC-LMS Algorithm 19
2.3.3 Conventional Linearly Constrained RLS Algorithm ………………………22
2.3.4 Conventional Linearly Constrained CM Gradient Algorithm …..23
2.3.5 Conventional Linearly Constrained CM RLS Algorithm……..…………...24
Chapter 3 Multi-User Multi-Carrier CDMA Detector with Diagonal Loading Linearly Constrained RLS Algorithms
3.1 Introduction 28

3.2 Diagonal Loading Linearly Constrained RLS Algorithm 29
3.3 Diagonal Loading Linearly Constrained Constant Modulus RLS Algorithm 32
3.4 Computer Simulation Results 39

Chapter 4 Conclusions 50

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