- ISBN:9787302585152
- 装帧:一般胶版纸
- 册数:暂无
- 重量:暂无
- 开本:其他
- 页数:228
- 出版时间:2021-08-01
- 条形码:9787302585152 ; 978-7-302-58515-2
本书特色
《窄带干扰和冲激噪声的抑制与消除关键技术研究(英文版)》介绍的新技术与新方法,有望为该无线通信、智慧照明物联网、智能电网信息通信等领域的科研工作者的进一步研究提供一定理论基础和技术储备。《窄带干扰和冲激噪声的抑制与消除关键技术研究(英文版)》介绍的内容可以为新一代宽带数字通信传输中窄带干扰与冲激噪声的抑制与消除提供相关理论基础、技术参考和相关工程应用示范,亦可作为通信、信号处理、优化理论等方向本科生、研究生相关方向的教学科研参考书,以及工程技术人员的技术参考书籍。 本书入选“清华大学优秀博士学位论文丛书”。
内容简介
现代通信系统对传输的可靠性、有效性和稳定性的需求与日俱增。然而,广泛存在的复杂化、随机化、高强度的窄带干扰与冲激噪声是系统性能的重大瓶颈,传统方法难以有效消除其影响。《窄带干扰和冲激噪声的抑制与消除关键技术研究(英文版)》针对这一技术难点,基于经典数字通信系统理论和新型稀疏恢复理论,从新型物理层帧结构设计、**时频联合交织、基于压缩感知的稀疏恢复等方面介绍窄带干扰与冲激噪声抑制与消除关键技术,适用于无线通信、电力线通信、智慧照明网络等多种系统。
目录
Contents
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
2.3 Narrowband Interference Model and Impulsive Noise Model 42
2.3.1 Narrowband Interference Model 42
2.3.2 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 Integer CFO 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 123
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 of Algorithms 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
作者简介
刘思聪,现任厦门大学信息科学与技术学院通信工程系助理教授。曾任华为技术有限公司网络技术研究部高级研究工程师。分别于2012年、2017年获得清华大学电子工程系电子信息科学与技术专业学士学位、信息与通信工程专业博士学位,评为清华大学优秀博士毕业生、清华大学优秀博士学位论文、北京市优秀毕业生。主要研究领域为宽带多媒体传输、通信系统噪声干扰抑制、稀疏信号处理、压缩感知、稀疏学习等。已发表36篇SCI/EI收录论文,获国家发明专利授权7项,为宽带电力线通信物理层国家标准起草人,担任多个IEEE权威期刊审稿人,兼任多项国际学术杂志客座编委和国际会议TPC成员。
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