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研究生:李彥賢
研究生(外文):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
論文名稱(外文):Adaptive Simplified Channel Estimation for Multiple Antenna OFDM Systems with Decision Feedback
指導教授:陳永芳陳永芳引用關係
指導教授(外文):Yung-Fang Chen
學位類別:碩士
校院名稱:國立中央大學
系所名稱:通訊工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2005
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 estimaitonRLSdecision feedbackMIMO OFDM
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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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[2] Y. (G) Li, “Simplified channel estimation for OFDM systems with multiple transmit antennas,” IEEE Trans. Wireless Commun., vol. 1, pp. 67-75, Jan. 2002.
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[4] I. Barhumi, G. Leus, M. Moonen, “Optimal training design for MIMO OFDM systems in mobile wireless channels,” IEEE Trans. Signal Processing, vol. 51, pp.1615-1624, June 2003.
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[8] H. Sari, G. Karam, and I. Jeanclaude, “Transmission techniques for digital terrestrial TV broadcasting,” IEEE Commun. Mag., vol. 33, pp. 100-109, Feb. 1995.
[9] J. G. Proakis, “Digital Communication,” 4th ed., New York, McGraw-Hill, 2000.
[10] J. Du and Y. (G) Li, “MIMO-OFDM channel estimation based on subspace tracking,” in Proc. 57th IEEE Vehicular Technology Conf., pp. 1084-1088, Apr. 2003.
[11] G. L. Stuber, J. R. Barry, S. W. McLaughlin, Y. (G) Li, M. A. Ingram, T.G. Pratt, “Broadband MIMO-OFDM wireless communications,” IEEE Proceedings, Vol. 92, pp. 271-294, Feb. 2004.
[12] P. T. Hwang, “An Adaptive Channel Estimation Scheme for MIMO OFDM System,” Master Thesis, National Central University, Jung-li City, Taiwan, R.O.C., Jun. 2004.
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