多天線正交分頻多工系統應用決策回授之調適性簡易通道估測

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研究生：	李彥賢 Yen-Hsien Lee
論文名稱：	多天線正交分頻多工系統應用決策回授之調適性簡易通道估測 Adaptive Simplified Channel Estimation for Multiple Antenna OFDM Systems with Decision Feedback
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：	93
語文別：	英文
論文頁數：	50
中文關鍵詞：	決策回授、調適性通道估測、遞迴最小平方演算法、多輸入多輸出正交分頻多工系統
外文關鍵詞：	adaptive channel estimaiton, RLS, decision feedback, MIMO OFDM
相關次數：	點閱：8 下載：0
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在高效率資料傳輸系統中，多輸入多輸出(MIMO)正交分頻多工系統(OFDM)是一個有效的資料傳輸技術。在這樣的系統，我們需要通道參數的資訊，以便解決資料多樣性連結、同調偵測和編解碼等問題。本論文中，我們使用調適性遞迴最小平方演算法(RLS)來估測通道，並且改進演算法的複雜度以增加使用上效能，我們還使用估測出的通道參數資訊來解調經由多餘循環檢查(CRC)編碼的資料，並用回授決策的方式更新通道參數。此外，我們對所提出的演算法做數學上的效能分析，並從中獲得在不同督普勒頻率(Doppler frequency)下的最佳的遺忘參數(forgetting factor)，最後的模擬結果也驗證了所提出的方法的有較好的平均平方誤差(MSE)和位元錯誤率(BER)。

Multiple-input and multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system is an effective technique for highly efficient data transmission. Channel parameters are required for diversity combing, coherent detection, and decoding. In this thesis, we propose an adaptive algorithm (RLS) for channel estimation in MIMO OFDM system and reduce its complexity to improve performance compared with the scheme in [1]. We employ the information of estimated channel parameters and demodulated data with CRC in the previous transmit block to update the channel information in a decision feedback fashion. Besides, we also obtain the optimum forgetting factor corresponding to different Doppler frequencies by analyzing the performance, and present the efficacy of the proposed scheme by simulation results.

Contents   I
List of Figures  III
Chapter 1 Introduction  1
Chapter 2 Wireless OFDM System  3
2.1 Introduction   3
2.2 OFDM Principle	   3
2.2.1 Multicarrier modulation   3
2.2.2 Orthogonal Frequency Division Multiplexing   5
2.2.3 Cyclic Prefix   7
2.2.4 Windowing   8
2.3 OFDM System Model   9
Chapter 3 MIMO OFDM System Model   11
3.1 MIMO OFDM System Model   11
3.2 Channel Statistics   12
3.3 Delay Profiles   13
3.4 Channel Equalization and Signal Demodulation   14
Chapter 4 Adaptive Channel Estimation   16
4.1 Basic Channel Parameter Estimation   16
4.2 Optimum training sequence   19
Chapter 5 Adaptive Simplified Channel Estimation   21
5.1 Adaptive Channel Estimation   21
5.2 Adaptive Simplified Channel Estimation   22
5.3 Multiple Antenna Application   23
5.4 Performance Analysis   26
5.5 Optimum Forgetting Factor λ   32
Chapter 6 Simulation Results   37
6.1 System Parameters   37
6.2 Simulation Results   38
6.2.1 MSE versus SNR performance comparison   38
6.2.2 BER and SER performance   41
6.2.3 Multiple antenna MSE performance comparison   45
Chapter 7 Conclusions   50
REFERENCE   51

                                

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