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研究生： 陳偉銘
Chen, Wei-Ming
論文名稱： 降低球體解碼複雜度之演算法研究
On the Complexity-Reduced Algorithms for Sphere Decoding
指導教授： 張名先
Chang, Ming-Xian
學位類別： 碩士
Master
系所名稱： 電機資訊學院 - 電腦與通信工程研究所
Institute of Computer & Communication Engineering
論文出版年： 2010
畢業學年度： 98
語文別： 英文
論文頁數： 43
中文關鍵詞： 多重輸入多重輸出 、球體解碼 、球體半徑 、樹狀搜尋
外文關鍵詞： MIMO, sphere decoding, sphere radius, tree search
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    • 具低複雜度之球體解碼演算法能解決多維度多重輸入多重輸出的最大概似解(ML)。在本篇論文中, 我們焦點放在降低球體解碼演算法的複雜度, 然而, 在降低它的複雜度同時, 權衡卻發生在另一個複雜度的提升。
    對於絕對ML 的球體解碼, 我們討論計它的計算量跟樹狀搜尋的拜訪點數之權衡變化; 而對於近似ML 的球體解碼, 我們討論它的計算量、樹狀搜尋的拜訪點數以及錯誤率的權衡變化。
    最後, 我們使用K-best 的概念以及估計的方法來減少運算量和樹狀搜尋的拜訪點數, 而錯誤率的結果是非常接近最大概似解。

    Sphere decoding algorithm solves high-dimension multiple-input multiple-output(MIMO) maximum likelihood(ML) detection problems with lower complexity. In this thesis, we focus on reducing the complexity when using sphere decoding. However, the tradeoffs happens and another complexity increases on the other side.
    For exact-ML sphere decoding, we discuss the tradeoffs between computations and visiting nodes of tree-search. For near-ML sphere decoding, we discuss the tradeoffs among computational complexity, visiting nodes of tree-search and bit error rate(BER)performance.
    Finally, we devote the reduced-complexity on visiting nodes and computations with the concept of K-best algorithm and estimation approach while the BER performance is very close to ML solution.

    Chinese Abstract I English Abstract II Acknowledgements III Contents IV List of Figures VI Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Organization of the thesis 2 Chapter 2 Detection Algorithms for MIMO Systems 3 2.1 System Model 3 2.2 Maximum-likelihood Detection 4 2.3 Zero-forcing Detection 4 2.4 Minimum Mean-square Error Detection 5 2.5 Simulation Result 5 Chapter 3 Introduction to Sphere Decoding 8 3.1 Sphere Decoding 8 3.2 Tree Search for Sphere Decoding 12 3.2.1 Depth-First Search 12 3.2.2 Breadth-First Search 14 3.2.3 Depth-First with Sibling Memory Search 16 3.3 Simulation Result 17 Chapter 4 TradeOffs in Sphere Decoding for Exact ML Detection 19 4.1 Using Smaller Radius for the Sphere Decoding 19 4.1.1 Introduction 19 4.1.2 Simulation Result 21 4.2 Differential Metric with Tree Search 23 4.2.1 Introduction to Differential Metric 23 4.2.2 Differential Metric with Tree Search 26 4.2.3 Simulation Result 27 4.3 Reordering the Channel Matrix to Reduce the Complexity 29 4.3.1 Introduction to the Reordering Channel Matrix 29 4.3.2 Simulation Result 29 Chapter 5 Sphere Decoding for Near ML detection 31 5.1 Simplified Norm Algorithm 31 5.1.1 Introduction to ℓ^p-norm 31 5.1.2 Simulation Result 33 5.2 A Reducing Visited Nodes Approach with K-Best Algorithm 35 5.2.1 System Model 35 5.2.2 QR Decomposition Interference Suppression Combined with Interference Cancellation 36 5.2.3 Introduction to K-Best Algorithm 37 5.2.4 Simulation Result 38 Chapter 6 Conclusion 41 Bibliography 42

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