博碩士論文 955201102 詳細資訊




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姓名 林鑫呈(Hsin-cheng Lin)  查詢紙本館藏   畢業系所 電機工程學系
論文名稱 適用於IEEE 802.11n之4×4多輸入多輸出偵測器設計
(A 4×4 MIMO Detector for IEEE 802.11n Systems)
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摘要(中) 在本論文中提出可改善傳統K-Best球面演算法的解碼性能的前瞻式架構。傳統的K-Best架構在進行解碼時, 在判斷存活路徑時僅就該層節點的PEDs進行判斷, 導致最大可能的解有一定的機率被排除在K個存活路徑之外, 容易造成錯誤的情況因而造成效能衰減。所提出的前瞻式架構有別於傳統K-Best架構的地方在於後者, 因此, 而前者也就是本論文所提出的前瞻式K-Best球面演算法在判斷時不僅以該層節點的PEDs進行判斷, 而是將該層節點延伸至下一層之子節點的PEDs拿來做判斷, 因為下一層子節點的PEDs所包含的兩層資訊遠多於只包含一層資訊的PEDs, 用來判斷將可提升存活路徑包含可能解的機率, 也就是說可以有效的提升效能。就模擬結果而言, 當K值皆為10時, 我們可以發現前瞻式架構的效能在錯誤率4×10-4左右的時候, 優於傳統K-Best演算法約4 分貝(dB)。在硬體的實作上, 採用管線式架構以達到高產出的目的, 同時以1-norm取代2-norm, 1’s complement取代2’s complement, 以及硬體共用等概念來降低硬體複雜度, 對於前瞻式架構中的前置排序處理模組來說, 此三種方法, 依據合成的結果可減少70%的複雜度。
摘要(英) A look-ahead algorithm that can improve the detection performance of the conventional K-Best sphere decoding algorithm is proposed in this thesis. In the conventional K-Best sphere decoder, which uses the partial Euclidean distance (PED) in the current layer to decide the K survival paths, the maximum likelihood (ML) solution may be expelled in the top layers and thus its performance is degraded. However, the proposed look-ahead technique uses not only the PEDs in the current layer, but also the PEDs of the best child node in the next layer. Because that the PEDs of survival nodes in two layers contain more information than the PEDs of the survival node in one single layer, the probability of reaching the ML solution can be increased. We can see that the one-layer look-ahead algorithm outperforms the conventional K-best algorithm about 4 dB when the BER is around 4×10-4. As to the hardware implementation, a pipeline architecture is employed to enhance the throughput. Moreover, we replace the 2-norm by 1-norm, 2’s complement by 1’s complement and also use common term extraction. With these techniques, the hardware of the pre-sorting process in the look-ahead unit can be reduced more than 70%.
關鍵字(中) ★ 多輸入多輸出 關鍵字(英) ★ MIMO
★ IEEE 802.11n
論文目次 目錄……………………………………………………………………………………i
圖示列表……………………………………………………………………………v
表格列表……………………………………………………………………………vii
第一章 ……………………………………………………………………………1
1.1 簡介………………………………………………………………………………1
1.2 動機………………………………………………………………………………1
1.3 論文組織…………………………………………………………………………2
第二章 IEEE 802.11n 系統介紹……………………………………………………3
2.1 發送機簡介………………………………………………………………………3
2.2 TGn 通道模型(TGn Channel Model)……………………………………………4
2.2.1 功率方位角分布(PAS)與方向角散開程度(AS)………………………6
2.2.2 入射方位角(AoA)與發射方位角(AoD)………………………………6
第三章 多輸入多輸出(MIMO)系統介紹………………………………………10
3.1 系統模型……………………………………………………………………10
3.2 多輸入多輸出信號編碼與解碼(MIMO Encoding and Decoding)…………11
3.2.1 空間多工(Spatial Multiplexing)………………………………………11
3.2.1.1 強制歸零(Zero-Forcing, ZF)…………………………………12
3.2.1.2 最大相似(Maximum Likelihood, ML)偵測法………………13
3.2.1.3 縱向貝爾實驗室分層時空編碼(Vertical Bell Laboratories Layered
Space-Time, V-BLAST)………………………………………………13
3.2.2 時空區塊編碼 (Space-Time Block Code, STBC)……………………16
第四章 球面解碼(Sphere Decoding)………………………………………………21
4.1 球面演算法(Sphere Algorithm)…………………………………………………21
4.1.1 簡介………………………………………………………………………21
4.1.2 球面限制(Sphere Constraint)……………………………………………22
4.1.3 樹狀搜尋(Tree Search)……………………………………………………22
4.1.3.1 深度優先(Depth-First)……………………………………………24
4.1.3.2 廣度優先(Breadth-first)…………………………………………25
4.1.3.3 最佳優先(Best-first)………………………………………………27
4.2 實數系統的球面演算法…………………………………………………………28
4.2.1 實數分解法(Real Value Decomposition)………………………………28
4.2.2 實數系統的列舉…………………………………………………………29
第五章 前瞻式K-Best 球面解碼器(Look-ahead K-Best Sphere Decoding)………32
5.1 傳統K-Best 球面解碼器的限制………………………………………………32
5.2 前瞻式K-Best 球面演算法(Look-ahead K-Best Sphere Algorithm) …………33
5.3 排序方式(Sort)…………………………………………………………………35
5.3.1 氣泡排序法(Bubble Sort)………………………………………………35
5.3.2 改良式合併排序法(Modified Merged Sort)……………………………36
5.3.3 列舉後排序法(Sort After SE-enumeration)……………………………39
5.4 複雜度計算 (Complexity Evaluation)…………………………………………40
5.5 模擬結果 (Simulation Results)…………………………………………………42
第六章 硬體實現……………………………………………………………………45
6.1 設計流程簡介…………………………………………………………………45
6.2 定點數決定………………………………………………………………………45
6.3 硬體架構設計……………………………………………………………………49
6.3.1 列舉後排序法(Sort After Enumeration)硬體設計………………………57
6.3.2 簡化式SE 列舉法 (Simplified SE-Enumeration) 硬體設計…………59
6.3.3 前置排序處理單元………………………………………………………61
6.3.4 其他組合邏輯計算單元…………………………………………………64
6.4 實現結果…………………………………………………………………………67
第七章 結論與展望…………………………………………………………………72
參考文獻……………………………………………………………………………73
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[20] Q. Li, Z. Wang, “An Improved K-Best Sphere Decoding Architecture for MIMO Systems,” IEEE Asilomar Conference on Signals, Systems and Computers, pp. 2190 – 2194, Oct. 2006.
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指導教授 蔡佩芸(Pei-yun Tsai) 審核日期 2009-7-24
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