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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 李枝宏 | |
| dc.contributor.author | Zih-Wun Hong | en |
| dc.contributor.author | 洪子文 | zh_TW |
| dc.date.accessioned | 2021-06-17T06:59:39Z | - |
| dc.date.available | 2022-08-13 | |
| dc.date.copyright | 2019-08-13 | |
| dc.date.issued | 2019 | |
| dc.date.submitted | 2019-08-04 | |
| dc.identifier.citation | [1] M. F. Tang, M. Y. Lee, B. Su and C. P. Yen, 'Beamforming-based spatial precoding in FDD massive MIMO systems,' 2014 48th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA,USA, pp. 2073-2077, 2014
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(2013). Antenna selection in measured massive MIMO channels using convex optimization. 2013 IEEE Globecom Workshops (GC Wkshps),Atlanta, GA USA 129-134, 2013. [37] I. Ahmed et al., 'A Survey on Hybrid Beamforming Techniques in 5G: Architecture and System Model Perspectives,' IEEE Communications Surveys & Tutorials, vol. 20, no. 4, pp. 3060-3097, Fourthquarter 2018 | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/72476 | - |
| dc.description.abstract | 基於本實驗室所提出之波束成型選擇空間預編碼(Beamforming-selection spatial precoding,BSSP)法的架構之下，為降低毫米波多輸入多輸出系統(mm-Wave multiple-input multiple-output,mm-Wave MIMO)中的硬體成本，本實驗室於先前提出了將BSSP結合傳送端之混合式預編碼(Hybrid Beamforming)的方法，而在本論文中進一步將接收端部分也運用混合式連接的架構，提出以二階段設計系統的方法，第一階段設計傳送端之預編碼矩陣(precoding matrix)，第二階段再設計接收端之接收器(combiner)參數，先以最小均方誤差(Minimum Mean-Square Error,MMSE)的解作為類比的接收器，再用第二代合作式共同粒子群最佳化法(Cooperatively Coevolving Particle Swarm Optimization,CCPSO)設計數位接收器的部分，我們發現這樣的設計方法比起只用MMSE之接收器可以有更好的效能表現。面對大規模多輸入輸出系統(Massive MIMO)中大量的天線元件伴隨著大量與射頻鏈(RF chain)之間的連線會造成傳輸過程中很多的傳輸損耗及運算成本，本論文在傳送端亦使用了部分連接(Partially connected)的架構，部分連接架構中包含不重疊子陣列(Non-overlapped Subarray,NOSA)及重疊子陣列(Overlapped Subarray,OSA)，不重疊子陣列意即每個波束群(Beam group)所連接的天線不會重疊，而重疊子陣列則是允許波束群之間所連接的天線有部分重疊，重疊子陣列架構是在全連接(Fully connected)及不重疊子陣列架構之間所做的取捨，本論文的實驗結果中將會展示不同連接方式之間的效能關係。 | zh_TW |
| dc.description.abstract | To reduce the hardware cost in mm-Wave multiple-input-multiple-output (mm-Wave MIMO) system, our laboratory previously proposed a Hybrid Beamforming architecture based on Beamforming-selection spatial precoding (BSSP). In this thesis, we further propose a two-step designing method used in the Hybrid Transceiver system. At the first stage, we design the analog precoding matrix as previous research in our laboratory. At the second stage, we use the Minimum Mean-Square Error(MMSE) solution as the analog combining parameters and use the second generation of Cooperatively Coevolving Particle Swarm Optimization (CCPSO2) to design the baseband combiner. We have found that using hybrid structure at the receiver side in our system can have better performance than only using MMSE solution for the combiner. Massive MIMO system equips a large number of antenna elements and links between antennas and RF chains that will cause a lot of transmission loss and high computation complexity. In this thesis, we used partially connected structure at the transmitter side. There are two kinds of partially connected structure, Non-overlapped Subarray(NOSA) and Overlapped Subarray(OSA). Non-overlapped Subarray means that the antennas connected by each Beam group do not overlap. Overlapped Subarray means that each Beam group’s connected antenna allows partial overlap. Overlapped Subarray can be seen as a trade-off between fully connected and Non-overlapped Subarray. The simulation results of this thesis will show the performance relationship between different connection methods. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T06:59:39Z (GMT). No. of bitstreams: 1 ntu-108-R06942103-1.pdf: 17412637 bytes, checksum: 4aed61e4851372c916af061296179d95 (MD5) Previous issue date: 2019 | en |
| dc.description.tableofcontents | 口試委員會審定書 i
誌謝 ii 摘要 iii ABSTRACT iv 目錄 v Chapter 1 緒論 1 1.1 研究背景 1 1.2 研究動機 3 1.3 論文貢獻 3 1.4 論文架構 4 Chapter 2 基於波束成形之空間預編碼(Beamforming-based spatial precoding，BBSP) 6 2.1 FDD MIMO System 6 2.2 BBSP之Downlink training 9 2.3 BBSP之CSI feedback及precoding流程 10 Chapter 3 合作式共同粒子群最佳化演算法與基於波束成型選擇之空間預編碼(Beamforming-selection spatial precoding，BSSP) 13 3.1 粒子群最佳化演算法(Particle Swarm Optimization，PSO) 13 3.2 PSO與BBSP計算Average SINR的比較 16 3.2.1 實驗參數及公式 16 3.2.2 實驗結果及分析 18 3.3 合作式共同粒子群最佳化演算法(Cooperatively Coevolving Particle Swarm Optimization，CCPSO2) 20 3.3.1 CCPSO2之演算法流程 20 3.4 BSSP-CCPSO2以平均理論位元錯誤率作為適應方程式(fitness function) 23 Chapter 4 BSSP-CCPSO2與混合式架構之BSSP-CCPSO2(Hybrid-BSSP-CCPSO2) 25 4.1 BSSP-CCPSO2的實驗方法及步驟 25 4.1.1 BSSP-CCPSO2之實驗步驟 26 4.2 BSSP-CCPSO2在3GPP通道下的平均位元錯誤率模擬 29 4.2.1 在3GPP之三種通道環境下的平均位元錯誤率模擬 29 4.2.2 受到交互耦合影響(Mutual Coupling Effect)之通道的平均位元錯誤率模擬 32 4.2.3 在不同天線數量的系統下之平均位元錯誤率模擬 35 4.3 混合式架構下之BSSP-CCPSO2 38 4.3.1 混合式系統架構模型 39 4.3.2 混合式架構結合BSSP-CCPSO2之方法 41 4.3.3 Hybrid-BSSP-CCPSO2之實驗步驟 43 4.4 NB=8、NU=2 之Hybrid-BSSP-CCPSO2系統平均位元錯誤率模擬 47 4.4.1 在Urban Macro環境下之平均位元錯誤率模擬 48 4.4.2 在Urban Micro環境下之平均位元錯誤率模擬 48 4.5 NB=8、NU=4 之Hybrid-BSSP-CCPSO2系統平均位元錯誤率模擬 49 4.5.1 在Urban Macro環境下之平均位元錯誤率模擬 51 4.5.2 在Urban Micro環境下之平均位元錯誤率模擬 52 4.6 NB=48、NU=2 之Hybrid-BSSP-CCPSO2系統平均位元錯誤率模擬 53 4.6.1 在Urban Macro環境下之平均位元錯誤率模擬 54 4.6.2 在Urban Micro環境下之平均位元錯誤率模擬 55 4.7 BSSP結合Quasi Orthogonal Space-Time Block Code(QOSTBC) 56 4.7.1 QOSTBC與BSSP之結合方法 56 4.7.2 BSSP結合QOSTBC後的平均理論位元錯誤率計算 59 4.7.3 Hybrid-BSSP-CCPSO2加入QOSTBC 60 4.7.4 Hybrid-BSSP-QOSTBC-CCPSO2實驗步驟 62 4.8 NB=8、NU=2的系統加入QOSTBC之系統平均位元錯誤率模擬 66 4.8.1 在Urban Macro環境下之平均位元錯誤率模擬 68 4.8.2 在Urban Micro環境下之平均位元錯誤率模擬 69 4.9 結論 70 Chapter 5 部分連接架構下的BSSP-CCPSO2(Partially Connected BSSP-CCPSO2) 71 5.1 部分連接架構之 BSSP-CCPSO2概念 72 5.2 部分連接架構之Hybrid BSSP-CCPSO2 73 5.2.1 部分連接架構Hybrid BSSP-CCPSO2實驗步驟 73 5.2.2 部分連接架構Hybrid BSSP-QOSTBC-CCPSO2實驗步驟 76 5.3 NB=8、NU=2之系統平均位元錯誤率模擬 80 5.3.1 NB=8，NU=2之系統在Urban Macro通道環境下之平均位元錯誤率模擬 81 5.3.2 NB=8，NU=2之系統在受到Mutual Coupling影響之Urban Macro通道環境下之平均位元錯誤率模擬 82 5.3.3 NB=8，NU=2之系統在Urban Micro通道環境下之平均位元錯誤率模擬 84 5.3.4 NB=8，NU=2之系統在受到Mutual Coupling影響之Urban Micro通道環境下之平均位元錯誤率模擬 85 5.4 NB=48、NU=2之系統平均位元錯誤率模擬 87 5.4.1 NB=48，NU=2之系統在Urban Macro通道環境下之平均位元錯誤率模擬 88 5.4.2 NB=48，NU=2之系統在受到Mutual Coupling影響之Urban Macro通道環境下之平均位元錯誤率模擬 89 5.4.3 NB=48，NU=2之系統在Urban Micro通道環境下之平均位元錯誤率模擬 91 5.4.4 NB=48，NU=2之系統在受到Mutual Coupling影響之Urban Micro通道環境下之平均位元錯誤率模擬 92 5.5 結論 94 Chapter 6 考慮空間相關性(Spatial Correlation)與交互耦合(Mutual Coupling)影響之通道及最佳化天線擺設之非均勻線性陣列(Nonuniform Linear Array，NLA ) 95 6.1 通道模型 96 6.1.1 線性陣列之空間相關性模型 96 6.1.2 交互耦合矩陣之模型 97 6.1.3 空間相關性與交互耦合共同影響之矩陣模型 98 6.2 最佳化天線位置擺設之方法 99 6.3 NB=8、NU=2之系統平均位元錯誤率模擬 101 6.3.1 受到空間相關性及交互耦合影響與最佳化天線擺設位置前後之平均互消息比較 103 6.3.2 傳送端天線全連接(Fully Connected)之平均位元錯誤率模擬 103 6.3.3 傳送端天線重疊子陣列連接(OSA Connected)之平均位元錯誤率模擬 105 6.3.4 傳送端天線不重疊子陣列連接(NOSA Connected)之平均位元錯誤率模擬 106 6.4 NB=32、NU=2之系統平均位元錯誤率模擬 108 6.4.1 受到空間相關性及交互耦合影響與最佳化天線擺設位置前後之平均互消息比較 110 6.4.2 傳送端天線全連接(Fully Connected)之平均位元錯誤率模擬 110 6.4.3 傳送端天線重疊子陣列連接(OSA Connected)之平均位元錯誤率模擬 112 6.4.4 傳送端天線不重疊子陣列連接(NOSA Connected)之平均位元錯誤率模擬 113 6.5 結論 115 Chapter 7 接收端以混合式連接架構之BSSP-CCPSO2 116 7.1 以CCPSO2設計用戶端接收器係數之模擬 117 7.1.1 BSSP-CCPSO2方法下接收器係數以CCPSO2設計之實驗步驟 118 7.1.2 BSSP-CCPSO2方法下加入QOSTBC接收器係數以CCPSO2設計之實驗步驟 126 7.1.3 NB=8、NU=2之系統平均位元錯誤率模擬 135 7.1.4 NB=32、NU=2之系統平均位元錯誤率模擬 138 7.2 考慮傳送端及接收端皆以混合式連接架構之BSSP-CCPSO2 141 7.2.1 傳送端及接收端皆以混合式連接架構之BSSP-CCPSO2實驗步驟 142 7.2.2 傳送端及接收端皆以混合式連接架構並加入QOSTBC之BSSP-CCPSO2實驗步驟 152 7.3 NB=8、NU=4之系統平均位元錯誤率之模擬 164 7.3.1 受到空間相關性及交互耦合影響與最佳化天線擺設位置前後之平均互消息比較 165 7.3.2 傳送端天線全連接之平均位元錯誤率模擬 166 7.3.3 傳送端天線以重疊子陣列連接之平均位元錯誤率模擬 168 7.3.4 傳送端天線以不重疊子陣列連接之平均位元錯誤率模擬 170 7.4 NB=32、NU=4之系統平均位元錯誤率之模擬 172 7.4.1 受到空間相關性及交互耦合影響與最佳化天線擺設位置前後之平均互消息比較 174 7.4.2 傳送端天線全連接之平均位元錯誤率模擬 175 7.4.3 傳送端天線以重疊子陣列連接之平均位元錯誤率模擬 177 7.4.4 傳送端天線以不重疊子陣列連接之平均位元錯誤率模擬 179 7.5 同時考慮傳送端及接收端之最佳化天線擺設位置 181 7.5.1 同時最佳化傳送端及接收端天線擺設位置之實驗步驟 181 7.5.2 NB=8、NU=4之系統平均位元錯誤率模擬 183 7.5.3 NB=32、NU=4之系統平均位元錯誤率模擬 187 7.6 結論 191 Chapter 8 考慮傳送端天線之部分連接架構最佳化設計 193 8.1 離散問題的最佳化方法 194 8.1.1 二進位制粒子群最佳化演算法(Binary Particle Swarm Optimization,BPSO) 194 8.1.2 直接表示法之離散粒子群最佳化演算法(Discrete Particle Swarm Optimization, Direct representation DPSO) 195 8.1.3 間接表示法之離散粒子群最佳化演算法(Discrete Particle Swarm Optimization, Indirect representation DPSO) 197 8.1.4 凸函數最佳化法(Convex Optimization,CVX) 198 8.2 不重疊子陣列(NOSA)連接之最佳化 198 8.2.1 利用DPSO將不重疊子陣列連接最佳化之方法 199 8.2.2 NB=8、NU=4不重疊子陣列經DPSO最佳化之系統平均位元錯誤率模擬 200 8.2.3 NB=32、NU=4不重疊子陣列經DPSO最佳化之系統平均位元錯誤率模擬 201 8.3 重疊子陣列(OSA)連接之最佳化 203 8.3.1 利用BPSO將重疊子陣列連接最佳化之方法 203 8.3.2 NB=8、NU=4重疊子陣列經BPSO最佳化之系統平均位元錯誤率模擬 204 8.3.3 NB=32、NU=4重疊子陣列經BPSO最佳化之系統平均位元錯誤率模擬 206 8.4 結合子陣列架構與預編碼設計之最佳化 207 8.4.1 結合子陣列架構與預編碼設計之最佳化方法 208 8.4.2 NB=8、NU=4結合不重疊子陣列與預編碼共同最佳化之系統平均位元錯誤率模擬 209 8.4.3 NB=32、NU=4結合不重疊子陣列與預編碼共同最佳化之系統平均位元錯誤率模擬 210 8.4.4 NB=8、NU=4結合重疊子陣列與預編碼共同最佳化之系統平均位元錯誤率模擬 212 8.4.5 NB=32、NU=4結合重疊子陣列與預編碼共同最佳化之系統平均位元錯誤率模擬 213 8.5 結論 215 Chapter 9 總結與未來方向 216 REFERENCE 218 | |
| dc.language.iso | zh-TW | |
| dc.subject | 空間預編碼 | zh_TW |
| dc.subject | 大規模多輸入多輸出系統 | zh_TW |
| dc.subject | 混合式波束成型 | zh_TW |
| dc.subject | 部分連接架構 | zh_TW |
| dc.subject | 粒子群最佳化演算法 | zh_TW |
| dc.subject | particle swarm optimization algorithm | en |
| dc.subject | Massive multiple-input-multiple-output system | en |
| dc.subject | spatial precoding | en |
| dc.subject | hybrid beamforming | en |
| dc.subject | partially connected | en |
| dc.title | 基於波束選擇方法之混合式大規模多輸入多輸出收發器設計 | zh_TW |
| dc.title | Hybrid Beamforming for Massive MIMO Transceiver Design based on Beam-Selection Approach | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 107-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 謝宏昀,劉玉蓀 | |
| dc.subject.keyword | 大規模多輸入多輸出系統,混合式波束成型,部分連接架構,粒子群最佳化演算法,空間預編碼, | zh_TW |
| dc.subject.keyword | Massive multiple-input-multiple-output system,hybrid beamforming,partially connected,particle swarm optimization algorithm,spatial precoding, | en |
| dc.relation.page | 222 | |
| dc.identifier.doi | 10.6342/NTU201902336 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2019-08-05 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電信工程學研究所 | zh_TW |
| 顯示於系所單位： | 電信工程學研究所 | |
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