一种新的TDCS峰均比抑制方法

论文摘要

由于具有类噪声性质的时域通信波形和抑制射频端干扰信号的能力，变换域通信（Transform Domain Communication System，TDCS）成为一种低截获率及低检测概率的通信系统。但TDCS一个主要的缺点是峰均比（Peak-to-Average Power Ratio, PAPR）过高问题。高峰均比将要求功率放大器具有较高的线性度，同时要求数模转换器具有较大的动态范围，这样将会导致系统成本的升高。本文引入凸集投影法（Projection onto Convex Sets，POCS）和梯度频带保留（Gradient based algorithm using tone reservation，TR）算法来降低PAPR。POCS算法是一种多载波调制中用于控制裁剪的算法。该算法通过反复向发射机中的未使用频带添加功率达到降低峰值功率的目的。在频域上，通过改变投影到复杂组合方法为多载波系统实现了时域成型矢量的产生。仿真结果显示：该算法有效地降低了PAPR，并且收发机波形匹配度并没有由于该算法的应用而恶化，另外，所获得的信号波形仍具有较低截获率。同时，本文研究了基于梯度频带保留的TDCS中PAPR抑制算法——TR算法。TDCS本质上是通过占用未使用频带传输信号的，而梯度频带保留算法降低传输信号PAPR的原理是利用未使用的或者预先保留的频带设计峰值切除信号。本文通过修改TDCS模型完成PAPR的抑制。理论分析基于梯度算法降低PAPR方法可知，通过应用强制预留空频带的TDCS模型将获得可接受的PAPR抑制效果。仿真结果显示：预留出128个TDCS频带中的12个（即9.4%），TDCS的PAPR将会获得4dB的降低。

论文目录

• 摘要
• Abstract
• Chapter 1 Summary of the Thesis
• 1.1 Background and Significance
• 1.2 Research Status of Correlatig Technologies
• 1.3 Thesis out Lines
• Chapter 2 Principle and Key Technologies of TDCS
• 2.1 Transform Domain Communication System
• 2.2 The Basic Principles of TDCS
• 2.3 TDCS Transmitter
• 2.3.1 Spectrum Estimation
• 2.3.2 Spectrum Magnitude （or Thresholding and Spectral Notching）
• 2.3.3 Phase Mapping Process
• 2.3.4 Inverse Transform and Buffer （Basis Function Generation and Modulation）
• 2.3.5 Modulation （Basis Function Generation and Modulation）
• 2.3.6 Transmission
• 2.4 TDCS Receiver
• 2.5 Previous Research Results
• 2.6 Summary
• Chapter 3 PAPR Reduction Techniques
• 3.1 Introduction to Peak-to-Power Ratio
• 3.2 High Power Amplifier
• 3.2.1 Soft Limiter
• 3.2.2 Solid State Power Amplifier
• 3.2.3 Traveling Wave Tube
• 3.3 PAPR Reduction Techniques
• 3.4 Categorization of the PAPR Problem
• 3.5 Overview of Existing Techniques
• 3.5.1 PAPR Reduction Techniques with Distortion
• 3.5.2 Tone Reservation
• 3.6 Summary
• Chapter 4 Projection onto Convex Sets
• 4.1 System Model
• 4.2 Performance Metrics
• 4.3 Gather and Polley Algorithm
• 4.4 Projection onto Convex Sets Algorithm
• 4.5 Simulation and Performances Results
• 4.5.1 PAPR Reduction Gain with POCS
• 4.5.2 Histogram of the Magnitude of the Correlation Coefficients Between the transmitted and received Waveforms
• 4.5.3 Results of Lilliefors and Jarque-Bera Testing for Normality
• 4.5.4 BER Performance
• 4.6 Summary
• Chapter 5 Gradient Based Algorithm using Tone Reservation
• 5.1 Gradient Based Algorithm
• 5.2 TDCS with Tone Reservation
• 5.2.1 Modified TDCS for Gradient Based Algorithm
• 5.2.2 Forced Null-Tones Spectrum Mask
• 5.3 Simulation and Performances Results
• 5.3.1 PAPR Reduction Gain with Gradient Based Algorithm using Tone Reservation
• 5.3.2 Effect of Number and Position of Reserved Tones
• 5.3.3 Selection Scheme of the Optimal Set in TR Method for PAPR
• 5.3.4 A Method using Variances Minimizing
• 5.3.5 Investigation of Number of Iteration Required to Achieve a Desired PAPR Reduction
• 5.3.6 Histogram of the Magnitude of the Correlation Coefficients Between the transmitted and received Waveforms
• 5.3.7 Results of Lilliefors and Jarque-Bera Testing for Normality
• 5.3.8 BER Performance
• 5.4 Summary
• Conclusion
• Recommendation for Future Research
• References
• Acknowledgement