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系統識別號 U0026-2311201220194500
論文名稱(中文) 利用子空間演算法之區塊傳送系統盲蔽式通道估測研究
論文名稱(英文) Exploration of Blind Channel Estimation Using Subspace-based Algorithms for Block Transmission Systems
校院名稱 成功大學
系所名稱(中) 電機工程學系碩博士班
系所名稱(英) Department of Electrical Engineering
學年度 101
學期 1
出版年 101
研究生(中文) 方士豪
研究生(英文) Shih-Hao Fang
學號 n28951578
學位類別 博士
語文別 英文
論文頁數 120頁
口試委員 指導教授-謝明得
口試委員-陳儒雅
口試委員-丁邦安
召集委員-黃穎聰
口試委員-蘇賜麟
口試委員-郭致宏
中文關鍵字 區塊傳送系統  盲蔽式通道估測  子空間演算法  重複索引  多輸入多輸出系統 
英文關鍵字 block transmission system  blind channel estimation  subspace algorithm  repetition index  multiple-input multiple-output system 
學科別分類
中文摘要 子空間式盲蔽通道估測演算法為區塊傳送系統(block transmission systems)中較受到討論的研究主題,主要是因為此方法不需要傳送額外的已知資訊,如前導航符號(preamble symbols)或導引符號(pilot symbols),因此可有效提高頻寬效益(bandwidth efficiency)。但原始應用於區塊傳送系統的訊號模型,在未加入額外冗餘資訊(redundancy)的情況下,並不能直接使用子空間式演算法,因為此訊號模型並不符合子空間式演算法的必要條件(necessary conditions)。本論文首先提出藉由傳送特定的訊號,來建構出應用於單輸入單輸出(single-input single-output, SISO)系統且符合子空間演算法必要條件的訊號模型(signal model)。利用所建構出之訊號模型後,通道脈衝響應(channel impulse response, CIR)將可有效率的利用子空間式盲蔽估測演算法估測出。
為了提高子空間式演算法的收斂速度,一般可使用重複索引(repetition index)方法來減少所需要的接收訊號數目。但原始重複索引方法所建構出之訊號矩陣,其滿秩(full rank)機率相當低,特別是使用低階數之調變方法時,此現象尤其明顯。為了改善此問題,本論文提出了多種重複索引方法;利用所提出之方法,所建構出之訊號矩陣擁有趨近於壹之滿秩機率。此外,所提出之方法除了可應用於單輸入單輸出系統之外,亦可應用於單輸入多輸出(single-input multiple-output, SIMO)及多輸入多輸出(multiple-input multiple-output, MIMO)區塊傳送系統。
除了演算法的建構,本論文亦推導了各種子空間演算法可實行之必要條件(necessary condition)。最後從模擬結果可得之,我們所提出之方法在不同通道環境與調變方法下,相較於文獻上其他子空間式盲蔽通道估測演算法,不但具有較低之均方誤差(normalized mean-squared error, NMSE),所計算出之位元錯誤率(bit error rate, BER)效能也較佳。
英文摘要 Subspace-based blind channel estimation technique is now one of popular research topics for block transmission systems since the preamble or pilot symbols are not required. However, conventional signal model of block transmission systems cannot be directly applied for the subspace-based blind channel estimation since the necessary conditions of the subspace method are not satisfied.
In this dissertation, subspace-based blind channel estimation algorithms for single-input single-output (SISO) block transmission systems with special input symbols are presented. With these special symbols, the corresponding signal model can be easily constructed for subspace-based blind channel estimation.
Conventional repetition index method can increase the convergence speed of the subspace channel estimation methods. However, the problem of low probability of full-row-rank signal matrix may result in the failure of channel estimation especially when low-order constellations are applied. To overcome this problem, different types of repetition index methods are proposed to increase the probability of full-row-rank signal matrices in this dissertation. Furthermore, the proposed methods are suitable for SISO, single-input multiple-output (SIMO), or multiple-input multiple-output (MIMO) systems.
The necessary conditions of the proposed blind channel estimation algorithms are derived in this dissertation. From simulation results, the proposed subspace-based blind channel estimation methods outperform conventional methods in normalized mean-squared error (NMSE) and bit error rate (BER) under various channel environments with different modulation schemes.
論文目次 1. INTRODUCTION 1
1.1 MOTIVATION 2
1.2 PREVIOUS WORKS 4
1.3 ORGANIZATION OF THE DISSERTATION 6
2. SIGNAL MODEL AND SUBSPACE METHODS REVIEW 7
2.1 SIGNAL MODEL FOR BLOCK TRANSMISSION SYSTEMS 7
2.1.1 Signal Model for SISO-OFDM Systems with/without CP 7
2.1.2 Signal Model for SIMO-OFDM Systems with/without CP 10
2.1.3 Signal Model for MIMO-OFDM Systems 12
2.1.4 Signal Model for SISO CP-SC Systems 15
2.2 REVIEW OF SUBSPACE-BASED BLIND CHANNEL ESTIMATION ALGORITHMS 17
2.2.1 Subspace Algorithm with Virtual Carriers 17
2.2.2 Subspace Algorithm with Repetition Index 19
3. BLIND CHANNEL ESTIMATION FOR SISO-OFDM SYSTEMS WITH PARTICULAR INPUT SYMBOLS 21
3.1 SUBSPACE ALGORITHM WITH REAL SYMBOLS 22
3.2 SUBSPACE ALGORITHM WITH PERIODIC SYMBOLS 26
3.3 SIMULATION RESULTS AND COMPARISON 30
3.4 SUMMARY 36
4. SUBSPACE-BASED BLIND CHANNEL ESTIMATION ALGORITHMS WITH REPETITION INDEX 37
4.1 BLIND CHANNEL ESTIMATION FOR SIMO-OFDM SYSTEMS WITHOUT CP 38
4.1.1 Proposed Signal Model 38
4.1.2 Discussion on the Range of P and Complexity Analysis 42
4.2 BLIND CHANNEL ESTIMATION FOR SIMO-OFDM SYSTEMS WITH CP 44
4.2.1 Proposed Signal Model 44
4.2.2 Discussion on the Range of P and Complexity Analysis 47
4.3 BLIND CHANNEL ESTIMATION FOR SIMO-OFDM SYSTEMS WITH/WITHOUT CP 49
4.3.1 Proposed Signal Model 49
4.3.2 Subspace-based Channel Estimator 55
4.3.3 Discussion on the Range of P and Complexity Analysis 57
4.3.4 Analysis of Proposed Signal Matrices 59
4.4 SIMULATION RESULTS AND COMPARISON 63
4.4.1 Simulation Results for SIMO-OFDM Systems without CP 63
4.4.2 Simulation Results for SIMO-OFDM Systems with CP 65
4.4.3 Simulation Results for SIMO-OFDM Systems with/without CP 67
4.5 SUMMARY 73
5. SUBSPACE-BASED BLIND CHANNEL ESTIMATION BY SEPARATING REAL AND IMAGINARY SYMBOLS FOR CP-SC SYSTEMS 74
5.1 PROPOSED SIGNAL MODEL 75
5.1.1 Signal Model with Real Symbols 75
5.1.2 Signal Model with Repetition Index Method 76
5.1.3 Subspace-Based Channel Estimator 78
5.1.4 Discussion on the Range of P and Complexity Analysis 80
5.2 SIMULATION RESULTS AND COMPARISON 82
5.2.1 Analysis of Probability of Full Row Rank 82
5.2.2 Performance Analysis 84
5.3 SUMMARY 88
6. BLIND CHANNEL ESTIMATION ALGORITHMS FOR MIMO-OFDM SYSTEMS 89
6.1 SUBSPACE-BASED BLIND CHANNEL ESTIMATION ALGORITHM WITH REPEATED TIME-DOMAIN SYMBOLS 90
6.1.1 Proposed Signal Model 90
6.1.2 Subspace-Based Channel Estimator 92
6.2 GENERALIZED SUBSPACE-BASED BLIND CHANNEL ESTIMATION ALGORITHM WITH REPETITION INDEX 94
6.2.1 Proposed Signal Model 94
6.2.2 Subspace-Based Channel Estimator 96
6.2.3 Analysis of Probability of Full Row Rank 98
6.3 GENERALIZED SUBSPACE-BASED BLIND CHANNEL ESTIMATION ALGORITHM WITH NEW SIGNAL PERMUTATION METHOD 99
6.3.1 Proposed Signal Model 99
6.3.2 Subspace-Based Channel Estimator 100
6.3.3 Analysis of Probability of Full Row Rank 101
6.4 SIMULATION RESULTS AND COMPARISON 104
6.4.1 Simulation Results for Repeated Time-domain Method 104
6.4.2 Simulation Results for Generalized Subspace Method 106
6.4.3 Simulation Results for New Signal Permutation Method 107
6.5 SUMMARY 109
7. CONCLUSIONS AND FUTURE WORK 110
7.1 CONCLUSIONS 110
7.2 FUTURE WORK 112
BIBLIOGRAPHY 113
PUBLICATION LIST 118
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