李學智臺灣大學：電信工程學研究所陳柏穎Chen, Po-YingPo-YingChen2007-11-272018-07-052007-11-272018-07-052006http://ntur.lib.ntu.edu.tw//handle/246246/58640隨著無線通訊多媒體服務的需求遽增，提供高速傳輸成為系統業者責無旁貸的任務。最近，多輸入多輸出傳輸技術已被證明，相對於傳統單一輸入單一輸出系統，它可以大幅提升系統容量。除了來自於空間分集的效能提升和傳輸距離的改善，這項技術利用多路徑傳播的特性，建立平行的等效通道來傳送資料，因而使系統在有限頻寬下，大大地提升傳輸速率。由於它的效能改善以及較高的頻譜使用效率，因此無論在行動通訊系統（3G Long Term Evolution）抑或無線區域網路（IEEE 802.11n），皆將多輸入多輸出技術納入下一代之傳輸標準。 有線與無線通訊最大的差異在於通道的不同。在無線通訊系統中，通道對於傳輸效能之優劣扮演了相當關鍵的角色。因此，掌握無線環境中的通道特性成為一項重要的課題。傳統上，單一輸入單一輸出的通道可以被兩個維度的統計特性所描述，即時域和頻域；而當我們考慮多天線系統架構時，第三個維度—亦即空間—上的通道特徵則成為另一項我們不可忽略且必須了解的因素。 一個精確的通道模型不僅幫助我們進一步透析無線電傳播的性質，並且它還能提供評估、甚至預測系統效能的方法。本論文將概要的介紹一些在文獻上已被提出的空間通道模型，並且完整地分析室內和室外多輸入多輸出通道的統計特性，另外還提出幾種較有效率的方法來產生多輸入多輸出通道。藉由通道模型的分析探討，我們能較容易地找出系統效能下降的原因，從而設計強健的傳送接收機來抵抗通道衰落或干擾。As the demand on wireless multi-media increases, it becomes indispensable for service providers to provide high-data-rate transmissions. Recently, multiple-input multiple-output (MIMO) technology is shown to have tremendous capacity enhancement over single-input single-output (SISO) systems. This technique gains from spatial diversity and increases transmission range. It also exploits multipath propagation to create parallel channels to convey information, and thereby the data rate is increased without consuming extra radio frequency. Due to its compelling performance enhancement and high spectral efficiency, the MIMO technology has been adopted by different wireless communications standards, no matter in cellular systems (3G Long Term Evolution) or in wireless local area networks (IEEE 802.11n task group). The greatest difference between wired and wireless communications lies on the channel. In wireless communication systems, channels play an important role on transmission performance. Therefore, it is essential for us to know well the channel behaviors in a wireless environment. Traditionally, the SISO channel is described by the statistics along two dimensions: time and frequency. As the multi-antenna technology is taken into account, to capture the additional characteristics along the third dimension—space—is necessary. Modeling the channel accurately can not only help us gain more insights about radio propagation but also provides an approach to evaluate or even predict system performance. This thesis gives an overview of spatial channel models developed in literature, and thoroughly analyzes the statistical behaviors of indoor and outdoor MIMO channels. We also propose several methods to generate MIMO channels more efficiently. By means of channel understanding, it will become easier to find out the causes of performance degradation and thereby design a robust transceiver to combat fading and interferences.Abstract I Contents III List of Figures VII List of Tables XI Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Thesis Overview 2 Chapter 2 Overview of Spatial Channel Models 5 2.1 Preliminaries of MIMO Channels 5 2.2 Geometry-Based Channel Models 7 2.2.1 One-Ring and Two-Ring Models 8 2.2.2 GBSBCM and GBSBEM 11 2.2.3 Combined Elliptical Ring Model 14 2.3 Distributed Scattering Model 15 2.4 Saleh-Valenzuela Model with AOA/AOD 18 2.5 Measurement-Based Channel Model 20 2.6 MIMO Channel Models in Standards 23 2.6.1 Spatial Channel Model for MIMO Simulations in 3G Cellular Systems 24 2.6.2 MIMO Channel Models in WLAN Systems 29 2.7 Summary 33 Chapter 3 Outdoor MIMO Channel Modeling 35 3.1 Introduction 35 3.2 Space-Time Correlation for MIMO Fading Signals 36 3.2.1 STC for NB Fading Signals in Macrocells 39 3.2.2 STC for NB Fading Signals in Microcells 40 3.2.3 STC for WB Fading Signals in Microcells 41 3.2.4 STC for WB Fading Signals in Macrocells 42 3.3 Generation of MIMO Fading Signals 43 3.4 Multiple Clusters in Macrocells 48 3.5 Simulations, Verification and Applications 50 3.5.1 Space-Time Correlations of MIMO Channels 51 3.5.2 Outdoor MIMO Capacity 59 3.5.3 Performance of DL-WCDMA with Tx. and Rx. Diversity 64 3.6 Summary 67 Chapter 4 Indoor NLOS MIMO Channel Modeling and Generation 69 4.1 Introduction 69 4.2 The NLOS Indoor Multipath Channel Model 70 4.3 Statistics for NLOS Indoor Channels 73 4.3.1 RMS Delay Spread 73 4.3.2 Space-Frequency Correlation 74 4.4 Efficient Channel Generation Methods 75 4.4.1 Autoregressive and Spatial Filtering (ARSF) Method 75 4.4.2 Convolution Method 78 4.5 Simulations, Verification and Comparison 81 4.6 Summary 89 Chapter 5 Indoor LOS MIMO Channel Modeling and Verification 91 5.1 Introduction 91 5.2 The LOS Indoor Multipath Channel Model 94 5.3 Statistics for LOS Indoor Channels 97 5.3.1 RMS Delay Spread 97 5.3.2 Space-Frequency Correlation 102 5.4 Generation of Indoor LOS Channels 103 5.5 Comparison with Measurement Data 108 5.6 Indoor MIMO Capacity 116 5.7 Summary 120 Chapter 6 Conclusion 123 6.1 Summary of This Thesis 123 6.2 Future Works 125 Appendix 127 Appendix I 127 Appendix II 132 Appendix III 133 Appendix IV 136 Appendix V 138 Appendix VI 141 References 145 Abbreviations 151 Author’s Publications 1532656909 bytesapplication/pdfen-US多輸入多輸出多路徑傳播通道模型通道認知通道產生通道量測空時相關函數空頻相關函數多輸入多輸出通道容量Multiple-input multiple-output (MIMO)multipath propagationchannel modelingchannel understandingchannel generationchannel measurementspace-time correlation functionspace-frequency correlation functionMIMO capacitythesishttp://ntur.lib.ntu.edu.tw/bitstream/246246/58640/1/ntu-95-F90942068-1.pdf