| 研究生: |
陳明傑 Chen, Ming-Jei |
|---|---|
| 論文名稱: |
編碼多天線正交頻域多工訊號使用期望值最大化演算法在時變通道下之偵測 Channel Estimation and Tracking for MIMO OFDM Systems Using EM Algorithm |
| 指導教授: |
蘇賜麟
Su, Szu-Lin |
| 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: | 2006 |
| 畢業學年度: | 94 |
| 語文別: | 英文 |
| 論文頁數: | 36 |
| 中文關鍵詞: | 期望值最大化演算法 、多天線正交頻域多工訊號 |
| 外文關鍵詞: | EM algorithm, MIMO OFDM, channel estimation |
| 相關次數: | 點閱:189 下載:1 |
| 分享至: |
| 查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
多天線正交頻域多工系統在通道的偵測上比單天線的系統要來得複雜得多,主要是因為多根天線傳送資料時,接收端收到的訊號是整個疊加在一起的,所以即使知道收到的訊號為何,也無法輕易的估測出通道的效應,若是系統應用在行動通訊上,由於通道會在短時間內改變,通道的偵測就更加困難。然而,期望值最大化演算法能利用疊代的計算,把多重的訊號各自分開,大大地降低運算的複雜度,在此篇論文中,我們將探討在時變通道中此演算法配合更正錯誤碼的整體效能。
For MIMO OFDM systems, channel estimation schemes have been mostly based on pilot-assisted approaches, assuming a quasi-static fading model that allows the channel to be constant for a block of symbols and change independently to a new realization. Using detected symbols as a pilot symbol is a general way to tracking time varying channel, but error propagation might happen. In this thesis, we exploit error control coding to correct detected symbols. With EM algorithm, we can derive a simple and effective way to track time varying channel in MIMO OFDM systems.
[1] “TGn Sync Proposal Technical Specification,” IEEE P802.11/889r6, May 2005.
[2] J.J van de Beek, O. Edfors, M. Sandell, S.K. Wilson and P.O. Borjesson, “On channel estimation in OFDM systems,” in Proc. IEEE VTC, Chicago, USA, vol. 2, pp. 815-819, Jul. 1995.
[3] W.C. Jakes, Jr., et al., Microwave mobile communications, New York: Wiley, 1974.
[4] R. G. Gallager, “Low Density Parity Check Codes,” IRE Trans. Inform. Theory, IT-8: 21-28, January 1962.
[5] R. G. Gallager, Low Density Parity Check Codes, MIT Press, Cambridge, 1963.
[6] A.P. Dempster, N.M. Laird, and D.B. Rubin, “Maximum likelihood from incomplete data via the EM algorithm,” J. Royal Statiscal Soc., Ser. B, vol, 39, no.1, pp.1-38,1977.
[7] For an extensive list of reference to papers describing applications of the EM algorithm, see http://www.engineering/usu.edu/Departments/ece/Publications/Moon on the World-Wide Web.
[8] C.Jiang, “The use of mixture models to detect effects of major genes on quantitative characteristics in a plant-breeding experiment,” Genetics, vol. 136, no.1, pp. 383-394, 1994.
[9] R. Render and H.F. Walker, “Mixture densities, maximum-likelihood estimation and the EM algorithm (review),” SIAM Rev., vol. 26, no. 2, pp. 195-237, 1984.
[10] J. Schmee and G.J. Hahn, “Simple method for regression analysis with censored data,” Technometrics, vol. 21, no.4, pp. 417-432, 1979.
[11] Y. Li, N. Seshadri, and L. Ariyavisitakul, “Channel estimation for OFDM systems with transmitter diversity in mobile wireless channel,” IEEE J. Select. Areas Commun, vol. 17, pp. 461-471, Mar. 1999.
[12] Y. Li, “Optimum training sequences for OFDM systems with multiple transmit antennas,” in Proc. IEEE Globecom ’00, vol. 3, San Francisco, CA, Nov. 2000, pp. 1478-1482.
[13] M. Feder and E. Weinstein, “Paraneter estimation of superimposed signals using the EM algorithm,” IEEE Transaction on Acoustics, Speech, and Signal Processing, vol. 36, pp. 477-489, 1988.
[14] Gao, Jie, “Channel Estimation and Data Detection for Mobile MIMO OFDM Systems,” Jan 2006.