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研究生: 郭韋麟
Kuo, Wei-Ling
論文名稱: 5G系統下非正交多重接取技術之性能分析
Performance Analysis of Non-Orthogonal Multiple Access for Fifth-Generation Communications
指導教授: 陳曉華
Chen, Hsiao-Hwa
學位類別: 碩士
Master
系所名稱: 工學院 - 工程科學系
Department of Engineering Science
論文出版年: 2019
畢業學年度: 107
語文別: 英文
論文頁數: 160
中文關鍵詞: 5GPD-NOMAPDMASCMA蒙地卡羅法
外文關鍵詞: 5G, PD-NOMA, PDMA, SCMA, Monte Carlo method
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  • 非正交多重接取技術(NOMA)是未來第五代通訊系統的核心接取技術。本論文回顧過去五年有關於NOMA的相關文獻,並且討論了關於各種不同的NOMA技術之系統架構,然後在這些基礎上進行研究。為了有利於讀者理解,前半部分從行動通訊的基礎概念、不同的非正交多重接取技術、至無線通道理論都進行了深入淺出的介紹。後半部分則是根據所選出的非正交多重接取技術候選者:功率域的NOMA、PDMA、SCMA,三者作了詳盡的性能分析和比較。過去大部分的文獻在比較不同種類的非正交多重接取技術時,常著重於分析不同的性能指標,如碼域著重於系統誤碼率,而功率域則著重於頻譜效率。我們著重於三個ITU提出於未來第五代通訊系統之性能指標:頻譜效率、傳輸延遲及用戶過載率。我們利用蒙地卡羅法分析碼域及功率域的非正交多重接取技術之頻譜效率,並且利用排隊理論建立端對端延遲模型,來探討系統傳輸時間和排隊時間之延遲,且探討系統用戶過載率對另外兩指標之影響。模擬結果分析出不同非正交多重接取技術在不同性能指標下之優劣,並提出各個非正交多重接取技術之最適用場景。

    The non-orthogonal multiple access (NOMA) is the key multiple access in the future fifth generation communication. We survey papers related NOMA in last five years. Also, we introduced the fundamentals of different NOMA in these papers and started this thesis based on their results. In order to make the thesis easy to understand, the first halthesis introduced the basic concept of cellular system, different NOMA schemes, and the wireless channel models. The second half is the main contribution for this thesis. The candidates of the chosen NOMA scheme: power-domain NOMA (PD-NOMA), pattern division multiple access (PDMA), and sparse code multiple access (SCMA) are compared by detailed performance analysis. Most previous references prefer on the specific performance index when they analyzed the specific types NOMA, where the bits error rate (BER) for the code-domain NOMAs and spectrum efficiency for power-domain types NOMAs. We focus on the three performance indices which is proposed by ITU, where the spectrum efficiency for eMBB, the user overloading rate for mMTC, and the latency for URLLC. The Monte Carlo method is used to model the spectrum efficiency for both code- and power-domain NOMA. The latency is modeled by the queuing theory with the queuing delay and transmitted delay. User overloading rate is a parameter for other performance indices. The result of the simulation shows the performance between different candidates under the performance indices. At last, we proposed the suitable scenarios for each NOMA candidates.

    摘要vii Abstract ix Acknowledgements xi Table of Contents xiii List of Figures xvii List of Tables xxv List of Abbreviations xxvii List of Symbols xxxi Dedication xxxiii 1 Introduction 1 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 A Brief Survey of Massive MIMO . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 Related Works and Motivation . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.4 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2 Overview of Non-orthogonal Multiple Access Techniques 11 2.1 Three Typical Usage Scenarios of 5G . . . . . . . . . . . . . . . . . . . . . 11 2.2 Classification of NOMA in Different Domain . . . . . . . . . . . . . . . . . 13 2.2.1 Power-Domain NOMA . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.2.2 PDMA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.2.3 MUSA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.2.4 SCMA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.2.5 RSMA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 2.2.6 IDMA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 2.2.7 IGMA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 3 Performance Analysis of Non-orthogonal Multiple Access Techniques 31 3.1 Candidate NOMA Schemes for 5G . . . . . . . . . . . . . . . . . . . . . . . 31 3.2 Radio Propagation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.3 Assumptions and Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . 40 3.4 Performance Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 3.4.1 Performance Indices . . . . . . . . . . . . . . . . . . . . . . . . . . 41 3.4.1.1 User overloading . . . . . . . . . . . . . . . . . . . . . . . 41 3.4.1.2 Spectral efficiency . . . . . . . . . . . . . . . . . . . . . . 42 3.4.1.3 Latency and Reliability . . . . . . . . . . . . . . . . . . . 43 3.4.2 Definition of SINRs in candidate NOMA schemes . . . . . . . . . . 48 3.4.3 Sum Capacity of OFDMA . . . . . . . . . . . . . . . . . . . . . . . 48 3.4.4 Performance Analysis of PD-NOMA . . . . . . . . . . . . . . . . . 49 3.4.4.1 SINR of PD-NOMA . . . . . . . . . . . . . . . . . . . . . 50 3.4.5 Performance Analysis of PDMA . . . . . . . . . . . . . . . . . . . . 56 3.4.5.1 SINR of PDMA . . . . . . . . . . . . . . . . . . . . . . . 58 3.4.6 Performance Analysis of SCMA . . . . . . . . . . . . . . . . . . . . 62 3.4.6.1 The Channel Model of SCMA . . . . . . . . . . . . . . . 66 3.4.6.2 The LLR Analysis of SCMA . . . . . . . . . . . . . . . . 67 3.4.6.3 The LLR Analysis of SCMA with MAP . . . . . . . . . . 68 3.4.6.4 The Mutual Information Analysis of SCMA . . . . . . . . 70 4 Performance Comparison of Non-Orthogonal Multiple Access 75 4.1 Major Parameters of Simulations in eMBB . . . . . . . . . . . . . . . . . . . 76 4.2 Simulations: Spectrum Efficiency . . . . . . . . . . . . . . . . . . . . . . . 77 4.3 Simulations: Latency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 4.4 Simulations: Bit Error Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 5 Conclusion and Future Works 95 5.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 5.2 Future Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 References 99 A The Variance of Signal Difference Between Before and After Equalizer 113 B Fundamentals of Multiple-Antenna Systems 117 B.1 Fundamentals of MIMO Space–Time Coded Wireless Systems . . . . . . . . 117 B.1.1 BER Analysis of Space–Time Coded Wireless Systems . . . . . . . . 121 B.1.2 Code Construction of Space–Time Trellis Codes . . . . . . . . . . . 124 B.1.3 Fundamentals of Space–Time Block Codes . . . . . . . . . . . . . . 126 B.2 Fundamentals of Massive MIMO Channel Modeling . . . . . . . . . . . . . 129 B.2.1 Channel estimation and pilot contamination . . . . . . . . . . . . . . 130 B.2.2 Channel Modeling of Massive MIMO . . . . . . . . . . . . . . . . . 131 B.2.2.1 Uplink Channel Modeling of Massive MIMO . . . . . . . 131 B.2.2.2 Downlink Channel Modeling of Massive MIMO . . . . . . 133 C Fundamentals of Massive MIMO Characteristics and Benefits 135 C.1 Favorable Propagation and Channel Hardening Phenomena when the Number of Antennas Grows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 C.2 Beamforming Techniques in Massive MIMO . . . . . . . . . . . . . . . . . 140 C.2.1 Channel Modeling Based on Beamforming Techniques . . . . . . . . 140 C.2.2 Directive Gain and Directivity . . . . . . . . . . . . . . . . . . . . . 143 C.2.3 Switched beamforming and adaptive beamforming . . . . . . . . . . 145 C.2.4 Blind adaptive beamforming and non-blind adaptive beamforming . . 146 C.2.5 Beamforming gain and diversity gain . . . . . . . . . . . . . . . . . 147 C.2.6 Beamforming in Massive MIMO . . . . . . . . . . . . . . . . . . . . 149 C.3 Detectors and Precoders in Massive MIMO . . . . . . . . . . . . . . . . . . 151 C.3.1 Matched Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 C.3.2 Zero-Forcing Receiver . . . . . . . . . . . . . . . . . . . . . . . . . 154 C.3.3 Minimum Mean Square Error Receiver . . . . . . . . . . . . . . . . 156 C.3.4 The Comparison of Linear Receivers . . . . . . . . . . . . . . . . . 159 C.3.5 Difference Between the Precoding and Beamforming . . . . . . . . . 159

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