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研究生： 黃伯廷
Po-Ting Hwang
論文名稱： 多輸入多輸出正交分頻多工系統之調適性通道估測
An adaptive channel estimation scheme for MIMO OFDM system
指導教授： 陳永芳
Yung-Fang Chen
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
畢業學年度： 92
語文別： 英文
論文頁數： 61
中文關鍵詞： 調適性通道估測 、訓練序列 、遞迴最小平方演算法 、多輸入多輸出正交分頻多工
外文關鍵詞： training sequence, RLS, MIMO OFDM, adaptive channel estimation
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    • 多輸入及多輸出正交分頻多工系統能在無線通訊中增加傳送資料容量。而對於頻率選擇性的衰退通道下，系統需要知道通道的參數資訊，以便於解回傳送的信號。本論文中，我們提出一種使用最佳訓練序列(training sequence)的調適性遞迴最小平方演算法(RLS)去估測通道，以改進其效能及降低複雜度。它是利用先前我們已經計算出的通道參數去幫助我們估測通道，而不去計算複雜度較高的反矩陣。最後，模擬結果也顯示出在較低的信號雜訊比(SNR)和較小的都卜勒頻率下，有較佳的均方誤差(MSE)和位元錯誤率(BER)。

    Multiple-input and multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems can be used to increase capacity in wireless communication. For the frequency-selective fading channel in wireband system, it is required to know the knowledge of channel parameters. In this thesis, we propose an adaptive recursive least-square (RLS) algorithm using optimum training sequences for channel estimation to improve the performance and reduce the complexity. Instead of tracking a large matrix inversion, we exploit the information of channel parameters that we have calculated to estimate the channel. Simulation results prove that the mean square error (MSE) performance of channel estimation and bit error rate (BER) are better with low signal- to-noise ratio (SNR) and low Doppler frequency.

    Contents Contents I List of Figures III Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Organization 3 Chapter 2 Introduction to OFDM Architecture 6 2.1 Multicarrier Modulation 6 2.2 OFDM Introduction and Block Diagram 7 2.3 Design of the OFDM Signal 10 2.3.1 Guard Interval 11 2.3.2 Windowing 12 2.4 Advantages and Disadvantages of OFDM System 13 Chapter 3 MIMO OFDM System Model 14 3.1 MIMO OFDM System Model 14 3.2 Characteristics of Time-Varying Channel Statistic 15 3.2.1 Multipath Propagation 15 3.2.2 Channel Statistics 17 3.2.3 Delay Profiles 19 3.3 Channel Equalization and Signal Demodulation 21 Chpater 4 Adaptive Channel Estimation 24 4.1 Basic of Channel Parameter Estimation 24 4.2 Optimum training sequence 27 4.3 Adaptive Channel Estimation 28 Chapter 5 Simulation Results 33 5.1 Description of Simulation 33 5.2 Simulation Results 34 5.2.1 Comparison between different adaptive channel estimation algorithms 34 5.2.2 Comparison between different channel estimation algorithms 44 Chapter 6 Conclusions 58 REFERENCE 59

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