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研究生:劉肖真
研究生(外文):Hsiao-Chen Liu
論文名稱:結合線性限制性常模反向QR分解遞迴最小平方和演算法以小波為基底之多載波分碼多重存取多用戶接收機之探討
論文名稱(外文):Wavelet-Based Multiuser MC-CDMA Receiver with Linearly Constrained Constant Modulus Inverse QRD-RLS Algorithm
指導教授:陳巽璋
指導教授(外文):Shiunn-Jang Chern
學位類別:碩士
校院名稱:國立中山大學
系所名稱:電機工程學系研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:英文
論文頁數:88
中文關鍵詞:強健線性限制性常模反向QR分解遞迴式最小平方和演算法分開線性干擾消除小波基底不匹配頻率補償盲目適應性演算法多使用者
外文關鍵詞:MismatchWavelet-basedMultiuserPLICFrequency offsetRobust LCCM-IQRD RLS AlgorithmBlind Adaptive Algorithm
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本論文中,我們探討多載波分碼多工系統及以小波為基底之多載波分碼多工系統在瑞雷衰退通道下藉著結合程序(combining process)以消除多重存取干擾(MAI)之問題。本論文的主要目的是在提出一個以線性限制性常模(Linearly Constrained Constant Modulus)為基本規範並結合強健線性反向QR分解(LCCM-IQRD)遞迴最小平方和演算法,更進一步提高傳統多載波分碼多工結合程序的系統效能。為了證實所提新演算法的優點,我們分別在通道參數估測不準確及頻率補償(frequency offset)的問題下做討論。
藉由模擬,我們可得知在不匹配(mismatch)情況下,強健線性限制性常模反向QR分解遞迴最小平方和演算法可改善傳統線性限制性常模梯度演算法的缺點,進而在MAI的消除能力,改善百分比指標(IPI),與位元錯誤率(BER)得到更好性能。另外,以小波為基底之多載波分碼多工系統較多載波分碼多工系統得到更好的性能。此外,強健線性限制性常模反向QR分解遞迴最小平方和演算法較其他傳統演算法,如:最大比例組合(Maximum Ratio Combining),線性限制性線性劃分干擾消除最小均方(linearly constrained Partitioned Linear Interference Canceller with Least Mean Square)逼近法,盲目適應性演算法(blind adaptive algorithm),線性限制性常模梯度演算法(linearly constrained constant modulus with gradient algorithm)在MAI的消除能力,與位元錯誤率(BER)得到較好性能


In this thesis, the problem of multiple access interference (MAI) suppression for the multi-carrier (MC) code division multiple access (CDMA) system, based on the wavelet-based (WB) multi-carrier modulation, associated with the combining process is investigated for Rayleigh fading channel. The main concern of this thesis is to derive a new scheme, based on the linearly constrained constant modulus (LCCM) criterion with the robust inverse QR decomposition (IQRD) recursive least squares (RLS) algorithm to improve the performance of the conventional MC-CDMA system with combining process. To verify the merits of the new algorithm, the effect due to imperfect channel parameters estimation and frequency offset are investigated.
We show that the proposed robust LCCM IQRD-RLS algorithm outperforms the conventional LCCM-gradient algorithm [6], in terms of output SINR, improvement percentage index (IPI), and bit error rate (BER) for MAI suppression under channel mismatch environment. Also, the performance of the WB MC-CDMA system is superior to the one with conventional MC-CDMA system. It is more robust to the channel mismatch and frequency offset. Moreover, the WB MC-CDMA system with robust LCCM IQRD-RLS algorithm does have better performance over other conventional approaches, such as the LCCM-gradient algorithm, maximum ratio combining (MRC), blind adaptation algorithm and partitioned linear interference canceller (PLIC) approach with LMS algorithm, in terms of the capability of MAI suppression and bit error rate (BER).


Acknowledgement i
Abstract ii
Contents iii
List of Figures and Tables v
Chapter 1 Introduction 1
Chapter 2 Wavelets and Filter Banks
2.1 Introduction 5
2.2 Fourier and Short-Time Fourier Transforms 6
2.3 Wavelet Transform 8
2.4 Discrete Wavelet Bases and Two-Band Filter Bank 10
2.5 Regular Binary Subband Tree Structure 13
2.6 Conclusions 16
Chapter 3 Conventional Techniques For MC-CDMA System With Combining Process
3.1 Introduction 22
3.2 System Model of MC-CDMA System 22
3.3 Optimal Weight Vector 29
3.4 Maximum Ratio Combining 32
3.5 Blind Adaptation Algorithm 32
3.6 Partitioned Linear Interference Canceller Approach 35
Chapter 4 Adaptive Robust LCCM IQRD-RLS Algorithm For WB MC-CDMA System With Combining Process
4.1 Introduction40
4.2 Wavelet-Based MC-CDMA System 41
4.3 Conventional Linearly Constrained Constant Modulus (CM) Gradient Detector46
4.4 Robust Linearly Constrained Constant Modulus (CM) IQRD RLS Algorithm47
4.5 Computer Simulation Results55
4.5.1 The Case of Perfect Channel Estimation56
4.5.2 The Mismatch Case 63
4.5.3 The Frequency Offset Case 67
4.5.4 The Frequency Offset under Mismatch Problem Case 71
Chapter 5 Conclusions74
Appendix A 76
Appendix B 78
Appendix C 80
Appendix D 81
Appendix E 83
References86


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