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1 Introduction

1.1 Research Background and Aimr/> 1.1.1 An Overview of Digital Communication Systemr/> 1.1.2 Noises and Interferencer/> 1.1.3 Characteristics and Detrimental Effects of NBI and IN

1.2 Related Works and Challenger/> 1.2.1 Related Works and Problems on NBI Mitigation

1.2.2 Related Works and Problems on IN Mitigation

1.3 Key Research Problems and Research Aimr/> 1.4 Main Works and Contributionr/> 1.5 Structural Arrangementr/> Referencer/>2 System Model and Fundamental Knowledge

2.1 An Overview of Broadband Digital Communication Systemr/> 2.1.1 OFDM-Based Block Transmission

2.1.2 Key Techniques of OFDM-Based Block Transmission

2.2 Frame Structure of Broadband Digital Communication Systemr/> 2.2.1 Structure of Preamble in Frame Header

2.2.2 Structure of Data Sub-Frame

2.3 Narrowband Interference Model and Impulsive Noise Model

2.3.1 Narrowband Interference Model

2.3.2 Impulsive Noise Model

2.4 Fundamentals of Sparse Recovery Theory

2.4.1 Compressed Sensing and Sparse Recovery

2.4.2 Structured Compressed Sensing Theory

2.4.3 Sparse Bayesian Learning Theory.

Referencer/>3 Synchronization Frame Design for NBI Mitigation

3.1 Introduction

3.1.1 Problem Description and Related Research

3.1.2 Research Aims and Problemr/> 3.2 Signal Model

3.3 Synchronization Frame Structure Design for NBI Mitigation

3.4 Timing and Fractional CFO Synchronization

3.5 Integer CFO Estimation and Signaling Detection with NBI

3.6 Performance Analysis of the Algorithmr/> 3.7 Simulation Results and Discussionr/> 3.8 Conclusion

Referencer/>4 Optimal Time Frequency Interleaving with NBI and TIN

4.1 Introduction

4.1.1 Problem Description and Related Research

4.1.2 Research Aims and Problemr/> 4.2 System Model

4.3 Design of Optimal Time-Frequency Joint Interleaving Method

4.3.1 Interleaving with Maximizing Time Diversity

4.3.2 Interleaving with Maximum Frequency Diversity

4.4 Performance Analysis of the Algorithmr/> 4.5 Simulation Results and Discussionr/> 4.6 Conclusion

Referencer/>5 Sparse Recovery Based NBI Cancelation

5.1 Introduction

5.1.1 Problem Description and Related Research

5.1.2 Research Aims and Problemr/> 5.2 System Model

5.3 Compressed Sensing Based NBI Reconstruction

5.3.1 System Model of Frame Structure

5.3.2 Temporal Differential Measuring

5.3.3 Compressed Sensing Based Reconstruction Algorithm

5.3.4 Simulation Results and Discussionr/> 5.4 Structured Compressed Sensing Based NBI Recovery

5.4.1 NBI and Signal Models in MIMO Systemr/> 5.4.2 Spatial Multi-dimensional Differential Measuring

5.4.3 Structured SAMP Algorithm

5.4.4 Simulation Results and Discussionr/> 5.5 Sparse Bayesian Learning Based NBI Recovery

5.5.1 System Model

5.5.2 BSBL Based NBI Reconstruction for CP-OFDM

5.5.3 Simulation Results and Discussionr/> 5.6 Performance Analysis of Algorithmr/> 5.7 Conclusion

Referencer/>6 Sparse Recovery Based IN Cancelation

6.1 Introduction

6.1.1 Problem Description and Related Research

6.1.2 Research Aims and Problemr/> 6.2 System Model

6.3 Prior Aided Compressed Sensing Based IN Cancelation

6.3.1 OFDM System Model with Impulsive Noir/> 6.3.2 Priori Aided Compressed Sensing Based IN Recovery

6.3.3 Simulation Results and Discussionr/> 6.4 Structured Compressed Sensing Based IN Cancelation

6.4.1 MIMO System Model with Impulsive Noir/> 6.4.2 Spatially Multi-dimensional IN Measurement

6.4.3 Structured Prior Aided SAMP (SPA-SAMP) Algorithm

6.4.4 Simulation Results and Discussionr/> 6.5 Compressed Sensing Joint Cancelation of NBI and IN

6.5.1 Time-Frequency Combined Measuring

6.5.2 Time-Frequency Combined Recovery of NBI and IN

6.5.3 Simulation Results and Discussionr/> 6.6 Algorithm Performance Evaluation

6.7 Conclusion

Referencer/>7 Conclusionr/> 7.1 Contributionr/> 7.1.1 Anti-NBI Frame Design and Synchronization Method

7.1.2 Optimal Time-Frequency Combined Interleaving

7.1.3 Sparse Recovery Based NBI and IN Cancelation

7.2 Further Research

Referencer/>

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