| 摘要 | 第4-5页 |
| ABSTRACT | 第5页 |
| Ⅰ INTRODUCTON | 第8-15页 |
| 1.1 BACKGROUND | 第8-9页 |
| 1.2 SIGNIFICANCE OF THE RESEARCH | 第9-10页 |
| 1.3 RESEARCH ABROAD AND ITS CURRENT LEVEL | 第10-12页 |
| 1.4 MAIN OBJECTIVES OF THE RESEARCH | 第12-13页 |
| 1.5 RESEARCH CONTENT | 第13页 |
| 1.6 RESEARCH METHOD | 第13-14页 |
| 1.7 RESEARCH INNOVATIONS | 第14-15页 |
| Ⅱ CHANNEL ESTIMATION IN MASIVE MIMO | 第15-28页 |
| 2.1 CONCEPT OF CHANNEL ESTIMATION | 第15-17页 |
| 2.1.1 Communication Channel Demands onMultimedia Services | 第15-16页 |
| 2.1.2 Mobile Wireless Channel and Massive MIMO | 第16-17页 |
| 2.2 MIMO CONCEPT AND ITS DIFFERENT CATEGORIES | 第17-21页 |
| 2.2.1 MIMO Concept in Channel Estimation | 第17-19页 |
| 2.2.2 MIMO in Different Categories | 第19-21页 |
| 2.3 CONCEPT OF MASSIVE MIMO IN CHANNEL ESTIMATION | 第21-27页 |
| 2.3.1 Introduction to Massive MIMO | 第21-24页 |
| 2.3.2 MIMO transmission in Flexible and Improved connection | 第24-26页 |
| 2.3.3 Importance of Channel Estimation | 第26-27页 |
| 2.4 CONCLUSION | 第27-28页 |
| Ⅲ MASSIVE MIMO IN 5G GENERATION | 第28-43页 |
| 3.1 EVOLUTION OF WIRELESS TECHNOLOGIES | 第28-32页 |
| 3.1.1 First Generation (1G) | 第28页 |
| 3.1.2 Second Generation (2G) | 第28-30页 |
| 3.1.3 Third Generation (3G) | 第30-32页 |
| 3.1.4 The Fourth Generation (4G) | 第32页 |
| 3.2 DEFINITION OF 5G | 第32-40页 |
| 3.2.1 The 5G and its Wide Range | 第33-36页 |
| 3.2.2 Comparative Analysis between 5G and 4G/LTE | 第36-37页 |
| 3.2.3 Some Physical Advantages with 5G | 第37-38页 |
| 3.2.4 The 5G and its Architecture | 第38-40页 |
| 3.3 LIMITATIONs IN 5G MASSIVE MIMO | 第40-41页 |
| 3.4 CONCLUSION | 第41-43页 |
| Ⅳ PROPOSED APPROACH BASED VB-BAYESIAN ALGORITHM AND MIMO MC-CDMA | 第43-52页 |
| 4.1 ORIGIN OF PROBLEMATIC | 第43-45页 |
| 4.2 ADOPTION OF BAYESIAN TECHNIQUE APPROACH | 第45-46页 |
| 4.2.1 The Role of Bayesian Method in the proposed Approach | 第45-46页 |
| 4.2.2 Main Contribution proposed | 第46页 |
| 4.3 MIMO TRANSMISSION IN FLEXIBLE AND IMPROVED CONNECTION | 第46-48页 |
| 4.3.1 MIMO transmission in Flexible and Improved connection | 第46-47页 |
| 4.3.2 System Model and ML-VB Interpretation | 第47-48页 |
| 4.4 REDUCTION OF MIMO PAPR EFFECT WITH SFBC SYSTEM MODEL | 第48-51页 |
| 4.4.1 PAPR System Model | 第48页 |
| 4.4.2 PAPR Proprieties | 第48-49页 |
| 4.4.3 PAPR Reduction via UR | 第49-51页 |
| 4.5 CONCLUSION | 第51-52页 |
| Ⅴ SIMULATION RESULTS | 第52-58页 |
| 5.1 MIMO REPRESENTATION BETWEEN EM AND VB PERFORMANCE | 第52-55页 |
| 5.2 EVALUATION OF MIMO FOR PEAK TO AVERAGE POWER RATIO (PAPR) | 第55-58页 |
| Ⅵ GENERAL CONCLUSIONS AND FUTURE WORKS | 第58-59页 |
| 6.1 GENERAL CONCLUSION | 第58页 |
| 6.2 FUTURE WORKS | 第58-59页 |
| REFERENCES | 第59-63页 |
| 致谢 | 第63页 |
