| 摘要 | 第1-8页 |
| Abstract | 第8-13页 |
| Abbreviation | 第13-14页 |
| 1 Introduction | 第14-22页 |
| ·Wide-band Cognitive Radio Networks | 第14-17页 |
| ·Background and Literature Review for Compressed Sensing Technology | 第17-19页 |
| ·Contributions and Thesis Organization | 第19-22页 |
| 2 Space-time Cooperated Compressed Spectrum Sensing in wide-band Homoge-neous Cognitive Radio Network | 第22-50页 |
| ·Introduction | 第22-24页 |
| ·Wideband Cognitive Radio Model | 第24-27页 |
| ·Compressed Spectrum Sensing and Bayesian Learning | 第27-34页 |
| ·Preliminaries | 第27-30页 |
| ·Compressed Sensing and Restricted Isometry Property | 第30-31页 |
| ·Bayesian Learning | 第31-34页 |
| ·Space-time Bayesian Compressed Sensing | 第34-42页 |
| ·Representing the Prior Information | 第34-39页 |
| ·Multi-prior Information | 第39-42页 |
| ·Non-sparse Spectrum Detection | 第42-43页 |
| ·Numerical Simulations | 第43-48页 |
| ·Comparison between BP, BCS and ST-BCCS | 第43-45页 |
| ·Performance Gains versus A priori Information | 第45页 |
| ·Bayesian Risk versus Sampling Rate | 第45-47页 |
| ·Non-sparsity detection | 第47-48页 |
| ·Conclusion | 第48-50页 |
| 3 Auto-clustering Collaborative Compressed Spectrum Sensing in Wide-band Heterogeneous Cognitive Radio Networks | 第50-68页 |
| ·Introduction | 第50-52页 |
| ·System model | 第52-55页 |
| ·System model of Compressed Sensing | 第52-53页 |
| ·System Model of Cooperation | 第53-55页 |
| ·Auto-clustering Collaborative Compressed Spectrum Sensing | 第55-62页 |
| ·Hierarchy Probabilistic Model Based Compressed Reconstruction | 第55-57页 |
| ·Dirichlet Process Mixture Model Based Cluster Member-ship Inference. | 第57-59页 |
| ·Proposed ACCSS algorithm | 第59-62页 |
| ·Simulation Results | 第62-67页 |
| ·Local Spectrum Sensing Performance | 第63-65页 |
| ·Sensing Performance over Entire WCR Network | 第65-66页 |
| ·Cluster Membership Estimation | 第66-67页 |
| ·Conclusion | 第67-68页 |
| 4 Belief Propagation based Cooperated Compressed Spectrum Sensing | 第68-98页 |
| ·Introduction | 第68-71页 |
| ·Compressed Spectrum Sensing Model | 第71-74页 |
| ·Cooperative Compressed Spectrum Sensing | 第74-84页 |
| ·Global Problem for Cooperative WCSS | 第74-75页 |
| ·Local Bayesian Compressed Sensing | 第75-80页 |
| ·Priori Information Abstraction | 第80-82页 |
| ·Cooperative BCS Reconstruction via Fused Priori Information | 第82-84页 |
| ·Priori Information Fusion via Belief Propagation | 第84-90页 |
| ·CR Network Correlation Modeling by MRF | 第84-86页 |
| ·Multi-Prior Fusion via Belief Propagation | 第86-90页 |
| ·Numerical Results | 第90-97页 |
| ·Local Spectrum Sensing Performance | 第90-93页 |
| ·Average Sensing Performance over Entire CR Network | 第93-94页 |
| ·Efect of Quantization and Packet Loss | 第94-96页 |
| ·Convergence of BP Algorithm | 第96-97页 |
| ·Conclusions | 第97-98页 |
| 5 Conclusion and Future Work | 第98-100页 |
| ·Conclusion of this thesis | 第98-99页 |
| ·Future Works | 第99-100页 |
| Acknowledgements | 第100-102页 |
| References | 第102-113页 |
| Achievements during Ph.D Study | 第113-115页 |
