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  • 音像窄带干扰和冲激噪声的抑制与消除关键技术研究(英文版)刘思聪
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    • 作者: 刘思聪著 | 刘思聪编 | 刘思聪译 | 刘思聪绘
    • 出版社: 清华大学出版社
    • 出版时间:2021-08-01
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    • 作者: 刘思聪著| 刘思聪编| 刘思聪译| 刘思聪绘
    • 出版社:清华大学出版社
    • 出版时间:2021-08-01
    • 版次:1
    • 印次:1
    • 页数:456
    • 开本:32开
    • ISBN:9787302585152
    • 版权提供:清华大学出版社
    • 作者:刘思聪
    • 著:刘思聪
    • 装帧:精装
    • 印次:1
    • 定价:119.00
    • ISBN:9787302585152
    • 出版社:清华大学出版社
    • 开本:32开
    • 印刷时间:暂无
    • 语种:暂无
    • 出版时间:2021-08-01
    • 页数:456
    • 外部编号:1202451633
    • 版次:1
    • 成品尺寸:暂无

    1 Introduction 1

    1.1 Research Background and Aims 1

    1.1.1 An Overview of Digital Communication Systems 3

    1.1.2 Noises and Interferences 7

    1.1.3 Characteristics and Detrimental Effects of NBI and IN 10

    1.2 Related Works and Challenges 13

    1.2.1 Related Works and Problems on NBI Mitigation 13

    1.2.2 Related Works and Problems on IN Mitigation 15

    1.3 Key Research Problems and Research Aims 18

    1.4 Main Works and Contributions 19

    1.5 Structural Arrangements 21

    References 24

    2 System Model and Fundamental Knowledge 31

    2.1 An Overview of Broadband Digital Communication Systems 31

    2.1.1 OFDM-Based Block Transmission 31

    2.1.2 Key Techniques of OFDM-Based Block Transmission 34

    2.2 Frame Structure of Broadband Digital Communication Systems 38

    2.2.1 Structure of Preamble in Frame Header 39

    2.2.2 Structure of Data Sub-Frame 41

    . Narrowband Interference Model and Impulsive Noise Model 42

    ..1 Narrowband Interference Model 42

    ..2 Impulsive Noise Model 46

    2.4 Fundamentals of Sparse Recovery Theory 49

    2.4.1 Compressed Sensing and Sparse Recovery 50

    2.4.2 Structured Compressed Sensing Theory 52

    2.4.3 Sparse Bayesian Learning Theory 55

    References 57

    3 Synchronization Frame Design for NBI Mitigation 61

    3.1 Introduction 61

    3.1.1 Problem Description and Related Research 61

    3.1.2 Research Aims and Problems 63

    3.2 Signal Model 63

    3.3 Synchronization Frame Structure Design for NBI Mitigation 65

    3.4 Timing and Fractional CFO Synchronization 66

    3.5 InterCO Estimation and Signaling Detection with NBI 69

    3.6 Performance Analysis of the Algorithms 71

    3.7 Simulation Results and Discussions 74

    3.8 Conclusion 77

    References 77

    4 Optimal Time Frequency Interleaving with NBI and TIN 79

    4.1 Introduction 80

    4.1.1 Problem Description and Related Research 80

    4.1.2 Research Aims and Problems 81

    4.2 System Model 82

    4.3 Design of Optimal Time-Frequency Joint Interleaving Method 83

    4.3.1 Interleaving with Maximizing Time Diversity 84

    4.3.2 Interleaving with Maximum Frequency Diversity 85

    4.4 Performance Analysis of the Algorithms 88

    4.5 Simulation Results and Discussions 90

    4.6 Conclusion 94

    References 96

    5 Sparse Recovery Based NBI Cancelation 99

    5.1 Introduction 99

    5.1.1 Problem Description and Related Research 99

    5.1.2 Research Aims and Problems 102

    5.2 System Model 103

    5.3 Compressed Sensing Based NBI Reconstruction 105

    5.3.1 System Model of Frame Structure 105

    5.3.2 Temporal Differential Measuring 109

    5.3.3 Compressed Sensing Based Reconstruction Algorithm 112

    5.3.4 Simulation Results and Discussions 117

    5.4 Structured Compressed Sensing Based NBI Recovery 1

    5.4.1 NBI and Signal Models in MIMO Systems 124

    5.4.2 Spatial Multi-dimensional Differential Measuring 125

    5.4.3 Structured SAMP Algorithm 128

    5.4.4 Simulation Results and Discussions 132

    5.5 Sparse Bayesian Learning Based NBI Recovery 136

    5.5.1 System Model 136

    5.5.2 BSBL Based NBI Reconstruction for CP-OFDM 141

    5.5.3 Simulation Results and Discussions 147

    5.6 Performance Analysis o Aorithms 151

    5.7 Conclusion 156

    References 157

    6 Sparse Recovery Based IN Cancelation 161

    6.1 Introduction 161

    6.1.1 Problem Description and Related Research 161

    6.1.2 Research Aims and Problems 162

    6.2 System Model 163

    6.3 Prior Aided Compressed Sensing Based IN Cancelation 165

    6.3.1 OFDM System Model with Impulsive Noise 165

    6.3.2 Priori Aided Compressed Sensing Based IN Recovery 166

    6.3.3 Simulation Results and Discussions 168

    6.4 Structured Compressed Sensing Based IN Cancelation 169

    6.4.1 MIMO System Model with Impulsive Noise 169

    6.4.2 Spatially Multi-dimensional IN Measurement 172

    6.4.3 Structured Prior Aided SAMP (SPA-SAMP) Algorithm 174

    6.4.4 Simulation Results and Discussions 176

    6.5 Compressed Sensing Joint Cancelation of NBI and IN 179

    6.5.1 Time-Frequency Combined Measuring 179

    6.5.2 Time-Frequency Combined Recovery of NBI and IN 182

    6.5.3 Simulation Results and Discussions 186

    6.6 Algorithm Performance Evaluation 190

    6.7 Conclusion 198

    References 199

    7 Conclusions 201

    7.1 Contributions 201

    7.1.1 Anti-NBI Frame Design and Synchronization Method 202

    7.1.2 Optimal Time-Frequency Combined Interleaving 203

    7.1.3 Sparse Recovery Based NBI and IN Cancelation 204

    7.2 Further Research 206

    References 208

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