| Abstract | 第4-5页 |
| Acknowledgements | 第6-13页 |
| 1 Introduction | 第13-22页 |
| 1.1 Prevailing Interference Management Techniques | 第15-16页 |
| 1.2 Spectrum Sensing in Cognitive Radio Networks | 第16-19页 |
| 1.2.1 Cognitive radio spectrum sensing challenge | 第17-18页 |
| 1.2.2 Proposed scheme | 第18-19页 |
| 1.3 Thesis Survey and Contributions | 第19-22页 |
| 2 Time-Frequency Analysis | 第22-30页 |
| 2.1 Stationary and Nonstationary Signals | 第23-24页 |
| 2.1.1 Stationary signals | 第23页 |
| 2.1.2 Nonstationary signals | 第23-24页 |
| 2.2 Linear and Quadratic Time-Frequency Analysis | 第24-28页 |
| 2.2.1 Linear time-frequency analysis | 第24-26页 |
| 2.2.2 Quadratic time-frequency analysis | 第26-28页 |
| 2.3 Summary | 第28-30页 |
| 3 Time-Reversal Communication Based PS-OFDM System Model | 第30-42页 |
| 3.1 Time-Reversal Communication Principles | 第30-31页 |
| 3.2 Multipath Propagation and Channel models | 第31-34页 |
| 3.2.1 Multipath channel model | 第33-34页 |
| 3.3 Deterministic and stochastic description of LTV channels | 第34-38页 |
| 3.3.1 Deterministic description of LTV channels | 第34-35页 |
| 3.3.2 Stochastic description of LTV channels | 第35-38页 |
| 3.4 PS-OFDM Time-Reversal Communication System Model | 第38-41页 |
| 3.4.1 System model | 第38-41页 |
| 3.5 Summary | 第41-42页 |
| 4 Channel Estimation for Time-Reversal Communication | 第42-61页 |
| 4.1 Time-Reversal Communication Challenges | 第43-44页 |
| 4.2 Characterization of Channel Impulse Response as a Local Scattering Function | 第44-47页 |
| 4.2.1 Correlation-underspread property for non-WSSUS LTV channels | 第45-46页 |
| 4.2.2 Example | 第46-47页 |
| 4.3 A Locally Stationary Processes Approach to Nonparametric Second-OrderChannnel Statistics Estimation | 第47-52页 |
| 4.3.1 Linear channel operator methods and the generalized Weyl symbol | 第48-50页 |
| 4.3.2 Generalized Wigner-Ville distribution and time-varying power spectrum | 第50-51页 |
| 4.3.3 Locally stationary processes | 第51-52页 |
| 4.4 Fast Karhunen-Loeve Basis Selection Algorithm | 第52-54页 |
| 4.5 Conventional Optimal Wiener Filter | 第54-56页 |
| 4.6 Design of a Frequency Domain Nonstationary Time-Varying Noncausal WienerFilter for Locally Stationary Processes | 第56-57页 |
| 4.7 Simulation Results | 第57-59页 |
| 4.8 Summary | 第59-61页 |
| 5 Improvement of Symbol Error Rate Using Discrete-Rate Adaptive Modu-lation Technique | 第61-71页 |
| 5.1 Adaptive Modulation Techniques | 第62-68页 |
| 5.1.1 Discrete-rate adaption technique | 第63-68页 |
| 5.2 Simulation Results | 第68-70页 |
| 5.3 Summary | 第70-71页 |
| 6 Location and Environment Awareness | 第71-79页 |
| 6.1 Conventional Location Estimation Algorithms | 第72-73页 |
| 6.1.1 TOA estimation | 第72页 |
| 6.1.2 DOA estimation | 第72-73页 |
| 6.1.3 RSSI estimation | 第73页 |
| 6.2 Proposed Algorithm | 第73-77页 |
| 6.3 Simulation Results | 第77页 |
| 6.4 Summary | 第77-79页 |
| 7 Wigner-Ville Distribution MVDR Beamforming Scheme | 第79-97页 |
| 7.1 Doppler Effect | 第81-82页 |
| 7.2 System Model | 第82-84页 |
| 7.3 Second-Order Statistics of the Observed Base Station Antenna Array Signal | 第84-85页 |
| 7.4 A Locally Stationary Processes Approach to Nonparametric Spectral Analysis | 第85-87页 |
| 7.4.1 Generalized Wigner-Ville Distribution and Time-Varying Spectrum | 第86页 |
| 7.4.2 Locally Stationary Processes | 第86-87页 |
| 7.5 Karhunen-Loeve Basis Selection Algorithm | 第87-89页 |
| 7.6 Non-Causal Wiener Filter Design for Locally Stationary Processes | 第89-90页 |
| 7.7 Conventional MVDR Beamformer | 第90-91页 |
| 7.8 Time-Frequency Wigner-Ville Distribution MVDR Beamformer | 第91-93页 |
| 7.9 Simulation Results | 第93-95页 |
| 7.10 Summary | 第95-97页 |
| 8 Conclusions and Future Work | 第97-101页 |
| 8.1 Summary of Results | 第98-100页 |
| 8.2 Future Research Topics | 第100-101页 |
| Bibliography | 第101-108页 |
| 作者简介及在学期间的研究成果 | 第108页 |
