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博碩士論文 etd-0708102-214807 詳細資訊
Title page for etd-0708102-214807
論文名稱
Title
利用適應性反向QR分解遞迴式最小平方和演算法做正交調變器與解調器之增益及相位不平衡與直流偏差之補償
Compensation For Gain/Phase Imbalance And DC Offset At Quadrature Modulator And Demodulator With Adaptive Inverse QRD-RLS Algorithm
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
54
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2002-06-21
繳交日期
Date of Submission
2002-07-08
關鍵字
Keywords
直流偏差、正交調變、增益與相位不平衡、反相QR分解遞迴式最小平方和演算法、預干擾器、適應性濾波器、補償
Inverse QRD-RLS Algorithm, Predistorter, Quadrature Modulation, Gain/Phase Imbalance, Adaptive Filter, DC Offset, Compensation
統計
Statistics
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The thesis/dissertation has been browsed 5777 times, has been downloaded 2022 times.
中文摘要
近來針對行動通訊的發射接收機的新設計有許多努力的成果,其中普遍的作法是結合射頻功能及數位訊號處理來得到線性的調變技術,並且使得調變器的樣式及接收機的處理能更具可塑性。但實際上,在正交調變器與解調器中,經常存在I-通道與Q-通道間的不平衡,這些不平衡主要是由於類比元件實現時,電容及電阻值的有限容忍度所導致,而這些I-通道與Q-通道間不可避免的不平衡降低了正交通訊技術的效能。
這篇論文中主要提出了一個新的盲目的架構,以及利用快速收斂的演算法,例如反相QR分解遞迴式最小平方和演算法(Inverse QRD-RLS),去解決以上所述在發射與接收機的補償問題。首先在發射機方面,我們將Inverse QRD-RLS演算法應用在一個利用功率量測作適應性估測及補償的方法,另外在接收機方面,利用量測機收信號的功率以及新的盲目適應性濾波器來達到非線性參數估測及補償的方法,被提出用來對正交解調器的增益及相位不平衡與直流偏差作適應性的補償。其中傳統的Inverse QRD-RLS演算法被使用來作參數估測及補償,並且不需要發射端傳送任何參考信號。在電腦模擬中,我們使用協調的16-PSK通訊系統來說明論文中所提出的架構之價值,而模擬結果證明我們所提出的方法相較於其他以存在的技術在消除不平衡與偏差之影響方面有相當大的改進,並且有較快的收斂速度以及在穩態時有較小的平均平方誤差。
Abstract
There has been much effort in new design for transceiver used in mobile communications. The general approach is to combine RF functions with DSP to allow linear modulation techniques and permit flexibility of modulation format and receiver processing. In practice, with the quadrature modulation technique there is always some imbalance between the I- and Q channels of modulator and demodulator. This is mainly due to finite tolerances of capacitor and resistor values used to implement the analog components. The unavoidable imbalance between the I- and Q channels is known to degrade the performance of quadrature communication system.
The main concern of this thesis is to propose a new blind scheme and with fast convergence algorithm, such as the inverse QRD-RLS algorithm, to deal with the problem described above for compensation in the transmitter and receiver. First, for the transmitter, the so-called adaptive estimation and compensation with power measurement implemented by the inverse QRD-RLS algorithm is employed. While in the receiver, a new blind adaptive filtering approach of the nonlinear parameters estimation and compensation, along with the power measurement in the receiver, is devised to adaptively compensate for the gain/phase imbalance and DC offsets in a quadrature demodulator. Where the conventional inverse QRD-RLS algorithm is employed for estimating the parameters of compensator, without using any reference signal transmitted from the transmitter. To document the merits of the proposed scheme, computer simulation for the coherent 16-PSK-communication system is carried out. With our proposed method a great improvement for eliminating the effects of the imbalance and offset over the existing techniques has verified. It has rapidly convergence rate and the smaller mean square error in steady state.
目次 Table of Contents
Acknowledgement i
Abstract ii
Contents iii
List of Figures v
Chapter 1 Introduction 1
Chapter 2 Conventional Adaptive Compensation Techniques With Direct Conversion Structure
2.1 Introduction 4
2.2 System Model Description 5
2.3 Adaptive Compensation With Envelope Detection For Transmitter
12
2.3.1 Adaptive Compensation Of DC Offset 14
2.3.2 Adaptive Compensation For Gain And Phase Imbalance 15
2.4 Decision Directed Compensation For Receiver 18
2.5 Linear Parameters Estimation And Compensation For Receiver 19
2.6 Nonlinear Parameters Estimation And Compensation For Receiver
22
Chapter 3 New Adaptive Compensation Algorithm And Scheme With Direct Conversion Structure
3.1 Introduction 25
3.2 A Volterra Predistorter Based On The Indirect Learning Architecture
26
3.3 Adaptive Estimation And Compensation With Power Measurement
31
3.4 Blind Adaptive compensation With Power Measurement 35
3.4.1 Parameter Estimation And Compensation 36
3.4.2 Recursive Implementation 40
3.5 Computer Simulation Results 41
Chapter 4 Conclusions 51
References
參考文獻 References
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