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研究生： 黃則惟
Huang, Tse-Wei
論文名稱： 一種基於格拉斯曼碼所設計可擴展規模之預先通道編碼技術
A Scalable Precoding Scheme based on Grassmannian Codebook for MU-MIMO
指導教授： 劉光浩
Liu, Kuang-Hao
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： 2017
畢業學年度： 105
語文別： 英文
論文頁數： 42
中文關鍵詞： 多用戶多輸入多輸出系統 、格拉斯曼編碼 、預先通道編碼 、低反饋率 、基因演算法 、通道相關性
外文關鍵詞： Multi-user MIMO, Grassmannian codebook, precoding, low feedback rate, genetic algorithm, channel correlation
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    • 本篇論文針對多用戶之多輸入多輸出系統提出一個格拉斯曼編碼方式來處理通道資訊反饋傳輸受限制的問題。在使用預先編碼的空間多工系統之中，基地台利用少量格拉斯曼編碼之反饋，將資料流分集給輸出天線，使訊號與干擾加雜訊比降低。本方法結合量化反饋的方式，使預先編碼系統在低訊雜比情況下效能提升。本方法利用格拉斯曼編碼解決通道相關性之問題，並使用基因演算法降低計算複雜度。
    低反饋的預先編碼方式分成兩種。當訊雜比低時，使用類比數位轉換器之量化反饋系統有較好的位元錯誤率。然而當訊雜比增加，量化雜訊會使位元錯誤率無法更低而達到飽和。使用格拉斯曼編碼方式可以降低訊號與干擾加雜訊比，卻因為選擇最佳編碼而增加系統複雜度。本方法使用基因演算法代替遍歷搜尋最佳編碼，使格拉斯曼編碼在用戶數較高的情形下，計算複雜度能被限制。另外，本方法使用壓擴技術，使格拉斯曼編碼能夠在通道具相關性時，有低的位元錯誤率。

    This thesis presents a precoding scheme based on Grassmannian codebook with low feedback rate in MU-MIMO systems. For a spatial-multiplexing system, the codebook-based precoding is attractive for its low feedback rate requirement. In this context, the codebook design based on Grassmannian line packing has been proposed for multi-user MIMO systems. However, Grassmannian codebook requires to operate at sufficiently high signal-to-noise ratio (SNR) and spatially uncorrelated channels.
    To address the aforementioned issues, the solution proposed in this work consists of two parts. To reduce the feedback rate requirement for MU-MIMO precoding, the channel state information (CSI) is quantized. However, the BER tends to be saturated in high SNR region due to quantization noise. And also when the channel independence is corrupted, companding is used in Grassmannian codebook precoding to make BER lower. On the other hand, searching the optimal codeword in the multi-user scenario incurs high computational complexity. To remedy the difficulty, a low-complexity and efficient searching method is proposed based on genetic algorithm (GA). Simulation results are presented to demonstrate the efficacy of the proposed precoding method for MU-MIMO systems.

    Table of Contents Chinese Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i English Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii Table of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi List of Symbols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii List of Acronyms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x 1 Introduction 1 1.1 Problem Statement and Literature Review . . . . . . . . . . . . . . . . . . 1 1.2 Thesis Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2 Related Work 3 2.1 Introduction to MIMO downlink . . . . . . . . . . . . . . . . . . . . . . . 3 2.2 Precoding Schemes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 3 System Model and Proposed Schemes 6 3.1 System Model of MU-MIMO . . . . . . . . . . . . . . . . . . . . . . . . . 6 3.1.1 Channel Estimation and Feedback . . . . . . . . . . . . . . . . . . 8 3.1.2 Channel Correlation . . . . . . . . . . . . . . . . . . . . . . . . . 9 3.2 Limited Feedback . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 3.2.1 Quantized Feedback Precoding . . . . . . . . . . . . . . . . . . . . 11 3.2.2 Codebook Precoding . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.3 Grassmannian Codebook Precoding . . . . . . . . . . . . . . . . . . . . . 15 3.3.1 Grassmannian Line Packing Criterion . . . . . . . . . . . . . . . . 17 3.3.2 MU-MIMO Grassmannian Precoding . . . . . . . . . . . . . . . . 19 3.4 Proposed Schemes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.4.1 Genetic Algorithm Optimization . . . . . . . . . . . . . . . . . . . 21 3.4.2 MU-MIMO Codebook Companding . . . . . . . . . . . . . . . . . 26 4 Simulation Results 27 4.1 MU-MIMO Precoding . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 4.1.1 Direct Precoding . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 4.1.2 Indirect Precoding . . . . . . . . . . . . . . . . . . . . . . . . . . 29 4.2 Proposed Schemes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 4.2.1 Composite Precoding . . . . . . . . . . . . . . . . . . . . . . . . . 30 4.2.2 Companding Codebook Precoding . . . . . . . . . . . . . . . . . . 34 4.2.3 Genetic Algorithm Optimization . . . . . . . . . . . . . . . . . . . 34 5 Conclusions and Future Work 38 5.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 5.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 References 40

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