博碩士論文 101521093 詳細資訊




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姓名 葉靖亨(Ching-heng Yeh)  查詢紙本館藏   畢業系所 電機工程學系
論文名稱 應用於多點協作多輸入輸出幾何平均分解之前編碼演算法及硬體設計
(Design and Implementation of GMD-based Precoding for Coordinated Multi-points (CoMP) Applications)
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摘要(中) 在3GPP LTE-Advanced中,利用OFDM的特性消除其內部干擾,但基地台間的干擾仍然存在。當使用者位於基地台的服務邊緣時,會受到鄰近的基地台訊號干擾,使得接收訊號的品質下降,而多點協作(Coordinated multiple points, CoMP)技術則被提出來解決這個問題。在此論文中,我們將幾何平均分解(GMD)應用於多點協作聯合處理(CoMP-JP)上,由於聯合處理之奇異質分解(JP-SVD)的效能會受到最弱的通道增益影響而衰減,為了獲得相同的空間通道增益,我們提出聯合處理之幾何平均分解(JP-GMD)來獲得所有協作之基站(cell sites)的前編碼矩陣和解碼矩陣,我們也使用球面解碼來達到最大相似解以追求更高的效能。同時,我們使用集中式的THP演算法消除剩餘的干擾,稱為JP-GMD-THP。相較於JP-SVD、JP-ZF、JP-MMSE,使用球面解碼之JP-GMD擁有最佳的效能。相較於使用球面解碼之JP-GMD,one-tap等化的JP-GMD-THP僅損失0.7dB的效能。為了減少每個基站的工作量和保有空間多樣性的優勢,我們提出兩種天線選擇技術,最佳選擇準則和分組選擇準則,相較於沒有協作(non-cooperative)之基站,兩種方法皆能有效地提高效能(bit error rate)。
在硬體設計方面則根據現有的穩定吞吐量(throughput)與高吞吐量的幾何平均分解進而設計出非方陣型的8×4幾何平均分解法,包含所提出的8×4雙對角矩陣分解架構、2×2 奇異值分解(SVD)和2×2幾何平均分解(GMD)以及提出新式非對角矩陣的更新法,使用Givens Rotation演算法來實現硬體,採用Systolic array的硬體架構,並利用管線化(pipeline)來提高吞吐量,而所提出的架構之吞吐量可達到每秒運算17.875M個矩陣(matrices/sec),且從比較表中可以看出,經正規化後所提出的硬體相較於其他相關作品有不錯的硬體效能表現。
摘要(英) In this thesis, we discuss the precoding schemes for coordinated multi-point (CoMP) joint processing (JP) using geometric mean decomposition (GMD). Unlike JP singular value decomposition (JP-SVD) whose performance is dominated by the spatial pipe with weak channel gain, JP-GMD is proposed to derive the precoding matrix of all the cooperative cell sites and the decoding matrix of the user equipment (UE) so that equal spatial channel gains can be obtained. Sphere decoding (SD) is then employed to achieve the maximum likelihood (ML) solution. Besides, centralized Tomlinson Harashima precoding (THP) can be adopted at the base station to remove interference, called JP-GMD-THP. We show that the JP-GMD plus SD scheme has significant performance improvement over JP-SVD, JP-zero forcing (ZF) and JP-minimum mean square error (MMSE). JP-GMD-THP that allows simple one-tap equalization has performance loss only about 0.7 dB compared to the JP-GMD plus SD scheme. Furthermore, to reduce the joint processing efforts at all cell sites and to take advantage of the spatial domain, two antenna selection techniques, best antenna selection and grouping antenna selection, are also proposed. As opposed to the best antenna selection, the grouping antenna selection technique can reduce search efforts with about 0.5 dB SNR degradation, but is still better than the non-cooperative schemes.
In hardware implementation, we design the architecture of JP-GMD according to our proposed algorithm. We simulate our proposed architecture by C language, and we implement our design in Verilog. A constant and high throughput GMD for matrix with of 8×4 is designed. The architecture of 8×4 JP-GMD includes 8×4 matrix bi-diagonalization, 2×2 SVD, 2×2 GMD and the proposed non-diagonal element updating. Givens Rotation algorithm is used to implement our design and systolic array architecture is adopted. Pipeline technique is employed to increase the throughput, which achieves 17.875M matrices/sec. From the comparison, the proposed architecture has good normalized throughput than the prior works.
關鍵字(中) ★ 多點協作
★ 幾何平均分解
★ 前編碼演算法
★ 多輸入輸出系統
★ 聯合處理
★ 天線選擇技術
★ 湯林遜哈洛希瑪前編碼演算法
★ 座標軸旋轉數位計數器
關鍵字(英) ★ Coordinated multi-points (CoMP)
★ Geometric mean decomposition (GMD)
★ Precoding
★ Multiple-input multiple-output (MIMO)
★ Joint processing (JP)
★ Antenna selection
★ Tomlinson-Harashima precoding
★ Coordinate Rotation Digital Computer (CORDIC)
論文目次 目錄 iv
圖示目錄 vii
表格目錄 xi

第一章 緒論 1
1.1 簡介 1
1.2 研究動機 1
1.3 論文組織 2
第二章 多點協作(Coordinated Multiple Points)多輸入輸出(MIMO)系統 3
2.1 介紹 3
2.2 中央式(Centralized)的聯合處理(Joint Processing)前編碼系統 4
2.2.1 單一使用者聯合處理奇異值分解 (JP-SVD)[4] 7
2.2.2 多使用者聯合處理強制歸零 (JP-ZF)[4] 8
2.2.3 多使用者聯合處理最小均方誤差 (JP-MMSE)[4] 8
2.2.4 多使用者聯合處理區塊對角化奇異值 (JP-BD-SVD)[5] 9
2.3 分散式(Distributed)的聯合處理(Joint Processing)前編碼系統 10
2.3.1 聯合處理QR分解 (JP-QRD)[6] 11
2.3.2 聯合處理Tomlinson-Harashima Precoding (JP-THP)[6] 12
2.3.3 排序的聯合處理 (Order JP)[6] 14
2.3.4 聯合處理相同比例能量分配 (JP Equal Rate Power Allocation)[7] 15
2.3.5 相同子通道能量分配THP (Modify JP-THP)[7] 16
2.3.6 排序的相同子通道能量分配THP (Best First Ordering)[7] 18
2.4 所提出的多點協作前編碼機制 20
2.4.1 單一使用者聯合處理幾合平均分解 (JP-GMD) 21
2.4.2 單一使用者聯合處理基於幾合平均分解的THP (JP-GMD-THP) 23
2.4.3 多使用者聯合處理基於區塊對角化幾合平均分解的THP (JP-BD-GMD-THP) 25
2.5 所提出的天線選擇技術 26
2.5.1 最佳選擇準則 (Best Selection Criterion) 27
2.5.2 分組選擇準則 (Grouping Selection Criterion) 27
2.6 模擬與比較 28
第三章 穩定吞吐量(Constant Throughput)與高吞吐量的幾何平均分解法設計 33
3.1 介紹 33
3.2 傳統的幾何平均分解法 35
3.3穩定吞吐量的幾何平均分解法 36
3.3.1 Complex-Value Givens Rotation 36
3.3.2 雙對角矩陣分解法 (Bi-diagonalization) 40
3.3.3 對角線等化法 42
3.3.3.1 無遞迴的2×2奇異值分解(SVD)運算 43
3.3.3.2 2×2幾何平均分解(GMD)運算 43
3.3.3.3 遞迴的對角線轉換法 44
3.3.3.4 2×2子矩陣運算法 46
3.4 所提出的幾何平均分解法 48
3.4.1 應用於多點協作的非方陣矩陣雙對角化 48
3.4.2 所提出的2×2幾何平均分解法 52
3.4.3 所提出的非對角矩陣的更新法 53
3.5 演算法比較 55
第四章 硬體架構設計與實現 57
4.1 硬體設計流程 57
4.2 硬體複雜度評估 58
4.3 8×4聯合處理幾何平均分解(JPGMD)的硬體 60
4.3.1CORDIC 62
4.3.2 計算4×4幾何平均分解(GMD)的CORDIC 65
4.3.3 計算幾何平均值的單元 67
4.3.4 Complex PE 69
4.3.5 8×4雙對角矩陣分解 72
4.4 4×4幾何平均分解的硬體 80
4.5 前編碼矩陣運算的硬體 86
4.6 決定CORDIC級數與Word Length 88
4.7 硬體實現與模擬 94
第五章 結論 98
參考文獻 99
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指導教授 蔡佩芸(Pei-yun Tsai) 審核日期 2015-7-14
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