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
內容試閱：
With the rapid development of broadband digital communications， the requirements for transmission reliability， effectiveness and stability keep increasing. However， the ubiquitously existing Barrowband Interference (NBI) and Impulsive Noise (IN) have become a vital bottleneck constraining the system performance of broadband communications systems. Due to the complicated characteristics of the NBI and IN that are different from additive white Gaussian noise， such as ran- domness， sparseness and high intensity， the conventional methods cannot eliminate their impacts effectively. Aimed at this technological dif?culty， this thesis is con- centrated on the main topic of “key technologies in NBI and IN mitigation and cancelation” based on the theories of digital communications systems and sparse recovery. The research is taken on the four aspects including frame structure， interleaving， sparse recovery and noise elimination:
First， concerning about the severe impacts of NBI on the synchronization of orthogonal frequency division multiplexing systems， the optimized frame structure design that can effectively mitigate the NBI impacts on synchronization is studied. Optimized synchronization algorithm is proposed to mitigate NBI， which signi?- cantly improves the accuracy of frame and carrier synchronization in the presence of NBI. Thus， a new signal frame structure for broadband transmission， which takes both spectral ef?ciency and transmission robustness into consideration， is formed. Second， considering about the drawback that conventional interleaving tech- niques cannot simultaneously mitigate NBI and IN effectively， the optimal time-frequency combined interleaving technology is studied. The techniques of the interleaving parameters optimization and the sub-matrix cyclic shifting for symbol interleaving are proposed to maximize both time and frequency diversity gains. The
performance of both anti-NBI and anti-IN capability is signi?cantly improved.
Moreover， to solve the crucial problem that conventional anti-NBI methods cannot exactly reconstruct the NBI， the technology of accurate NBI reconstruction based on the sparse recovery theory is researched on by exploiting the sparse property of NBI in the frequency domain. The Compressed Sensing (CS) and Structured CS (SCS)-based recovery algorithms are proposed. The spatially multi-dimensional SCS-based recovery algorithm for MIMO systems is proposed.
The research in sparse recovery theory-based NBI estimation is insuf?cient at present， so this thesis proffers cutting-edge and novel technology in this ?eld to improve the performance of NBI estimation signi?cantly， which can be widely applied to many different broadband transmission systems such as power line and wireless communications.
Finally， aimed at solving the drawbacks of the existing anti-IN methods such as high complexity， low spectral ef?ciency and inaccuracy， the technology of multi-dimensional CS-based IN cancelation is studied by exploiting the sparse property of IN in the time domain. The prior information aided CS-based method， along with the spatially multi-dimensional SCS-based method for IN cancelation， is proposed to effectively guarantee the reliable and ef?cient broadband transmission in the channel with severe noise and interference.
Through these researches， this thesis provides theoretical basis and technological essentials for the NBI and IN mitigation and cancelation in the next-generation broadband digital communications， and facilitates the application and standard- ization of the proposed technologies.
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