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研究生: 黃楷捷
Kai-Chieh Huang
論文名稱: 多重輸入多重輸出之正交分頻多工系統中對聯合載波頻率和通道估測的疊代演算法設計
Maximum-Likelihood-based Iteration Algorithm Design for Joint CFO and Channel Estimation in MIMO-OFDM Systems
指導教授: 陳永芳
口試委員:
學位類別: 碩士
Master
系所名稱: 資訊電機學院 - 通訊工程學系
Department of Communication Engineering
論文出版年: 2018
畢業學年度: 106
語文別: 英文
論文頁數: 41
中文關鍵詞: 聯合最大可能性估測載波頻率偏移估測多輸入多輸出系統正交分頻多工系統通道估測
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  • 這篇論文探討了在多重輸入多重輸出之正交分頻多工系統中利用最大可能性準則做載波頻率偏移和通道響應的聯合估測。初步介紹此系統的訊號模型以及基於最大可能性的聯合估測器推導,此演算法可以分成兩大部分,第一部分是利用旋轉觀念對載波頻率偏移作初始的估測,再利用初始結果設計頻域等化器。第二部分則是利用疊代的觀念來找出真正最大的頻率波峰,使載波頻率偏移估測的效果能更好。此疊代演算法可以正確收斂至全域最大值而不是區域最大值,且與格子搜尋法相比減少計算複雜度,模擬效能表現出來在均方誤差精確度上也貼近於克拉瑪界線。


    In this thesis, we present a joint carrier frequency offset (CFO) and frequency selective channel response estimation in multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems. The signal model of MIMO-OFDM is introduced and the joint estimator is derived based on the maximum likelihood criterion. The proposed algorithm can be separated into two major portions. In the first part of proposed algorithm, we estimate an initial CFO by using the rotation concept and utilize the result to design the frequency domain equalizer. The second part is to employ iterative concept to find the true frequency peak for better CFO estimation performance. The proposed method can surely converge to the global maximum solution step by step. In addition, the computation complexity is less than the grid-search method. The MSE performance of the proposed algorithm shown in the simulation result is close to the Cramer-Rao Lower bound (CRB).

    論文摘要 I Abstract II 致謝 III Contents IV List of Figures V List of Tables VI 1.Introduction 1 2.System Model 6 2.1 Transmitter 6 2.2 Wireless Channel 7 2.3 Receiver 7 3.Maximum Likelihood Carrier Frequency Estimation 10 4.Proposed New Algorithm Scheme 13 4.1 Mathematical Derivation 13 4.2 Initial CFO Estimation 16 4.3 Frequency Domain Equalizer Design 17 4.4 Small Step Iterative Searching 18 5.Simulation Result 22 5.1 Simulation Parameters for OFDM System 22 5.2 Algorithm Performance 23 6.Conclusion 27 7.Reference 28

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