Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/7800
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dc.contributor李彥文zh_TW
dc.contributor溫志煜zh_TW
dc.contributor.advisor吳國光zh_TW
dc.contributor.author吳哲安zh_TW
dc.contributor.authorWu, Che-Anen_US
dc.contributor.other中興大學zh_TW
dc.date2008zh_TW
dc.date.accessioned2014-06-06T06:40:33Z-
dc.date.available2014-06-06T06:40:33Z-
dc.identifierU0005-2908200715253000zh_TW
dc.identifier.citation[1] K. Pahlavan, A. H. Levesque, “Wireless Information Networks”, Wiley-Interscience, 1995 [2] William C. Jakes, “Microwave Mobile Communications.” New York: John Wiley & Sons Inc, 1975 [3] Harry L.Van Trees, “Detection, Estimation, and Modulation Theory”, John Wiley & Sons Inc, 2001 [4] J. R. Treichler, C. R. Johnson, M. G. Larimore, “Theory And Design of Adaptive Filters,” Prentice Hall, 2001 [5] B. R. Saltzberg, ”Performance of an efficient parallel data transmission system,” IEEE Trans. Commun. Technol., vol. COM-15, pp. 805-81 I , Dec. 1967. [6] S. B. Weinstein and P. M. Ebert, “Data transmission by frequency multiplexing using the discrete Fourier transform,” IEEE Trans. Commun. Technol.. vol. COM-19, pp. 628-634, Oct. 1971. [7] Clark R. H., “A Statistical Theory of Mobile-Radio Reception”, Bell Systems Technical Journal, vol.47, pp.957-1000, 1968 [8] P. Dent, G. E Bottomley, and T. Croft, “Jakes Fading Model Revisted” 24th Electronics Letters, vol.29, no.13, pp.1162-1163, Jun. 1993 [9] J. J. van de Beek, O. Edfors, M. Sandell, S. K. Wilson, and P. O.Borjesson, “On channel estimation in OFDM systems,” in Proc. IEEE Vehicular Technology Conf., vol. 2, Chicago, IL, July 1995, pp. 815-819. [10] O. Edfors, M. Sandell, J. van de Beek, S. K. Wilson, and P. O. Borjesson, ”OFDM channel estimation by singular value decomposition,” IEEE Trans. Commun, vol. 49, pp. 921-939, Judy 1998. [11] S. Coleri, M. Ergen, A. Puri, A. Bahai, ”Channel estimation techniques based on pilot arrangement in OFDM Systems”, IEEE Trans. on Broadcasting, 2002, 48(3):223-229. [12] Y. Zhao and A. Huang, “A novel channel estimation method for OFDM mobile communication systems based on pilot signals and transform-domain processing”, Proceeding of Vehicular Technology Conference, 1997 IEEE 47th, vol.3 pp. 2089-2093, 1997 [13] B. Yang, Z. Cao, and K, B. Letaief, “Analysis of low-complexity windowed DFT-based MMSE channel estimator for OFDM systems,” IEEE Trans. Commun, vol. 49, pp. 1977-1987, Nov. 2001 [14] D. Fraser, “Interpolation by the FFT revisited—An experimental investigation”, IEEE Trans. Acoust., Speech, Signal Processing, vol. 37, pp.665-675, May 1989. [15] Wen-Rong Wu and Po-Cheng Chen, “Adaptive AR Modeling in White Gaussian Noise,” IEEE Trans. Signal Processing, Vol. 45, pp. 1184-1192, May 1997 [16] M. Morelli and U. Mengali, “A comparison of pilot-aided channel estimation methods for OFDM systems,” IEEE Transactions on Signal Processing, Volume 49, Issue 12, Dec. 2001 pp. 3065~3073.zh_TW
dc.identifier.urihttp://hdl.handle.net/11455/7800-
dc.description.abstract在本篇論文中,我們提出一個二維的OFDM通道估計演算法。在OFDM (Orthogonal Frequency-Division Multiplexing)系統中,接收端可選擇使用同調檢測或是非同調檢測。在希望達到相同的錯誤率的情形下,非同調檢測的SNR值必須高出同調檢測達3 dB之多。而對於接收端而言,通道狀態的資訊(channel state information,CSI)是完成同調檢測的必要資訊。而在OFDM系統中,最常使用來取得CSI的方法就是通道估計,而此方法是基於一經過特別調製(training)的傳送訊號來執行,而且此訊號在接收端也設為已知。在經過training的訊號中會含有所謂的引領訊號(pilot),而引領訊號傳送的模式主要分為兩種,一種是持續在每此傳遞的資料中加入pilot,另一種則是僅在每個封包前面的位置配置一段連續的pilot。一般來說,通道估計的第一步就是藉由我們在接收端已知的training訊號,經由最小平方法(Least-Square,LS)來得出在傳送pilot的子載波(pilot-tone)上的通道估計值。接下來,我們再對於每個產生的估計值乘上由頻域相關性所產生的MMSE權重值(weighting)。為了能夠進一步的降低錯誤率,我們提出一加入時域相關性的通道估計演算法。我們使用ρ—LMS演算法去對每個傳送pilot的子載波均建立各自的自回歸(AutoRecursive,AR)模型。藉由AR模型的建立,每個傳送pilot的子載波均能得到一使用條件期望值的估計結果,而AR模型也會隨著估計值的產生而更新組合係數。而最後模擬的結果顯示,在SNR = 25 dB的情形下,使用結合頻域及時域相關性的創新演算法可使得MSE下降大約5 dB。zh_TW
dc.description.abstractIn this thesis, we propose a two-dimension channel estimation algorithm. Orthogonal frequency-division multiplexing(OFDM) systems employing coherent detection can achieve a 3dB SNR gain over differential detection. The channel state information(CSI)will be needed at the receiver to perform coherent detection. A widely used approach for obtaining CSI in OFDM systems is training data-based channel estimation. The training data consists of pilot symbols that are continuously multiplexed into the data stream, or a training data block at the beginning of each packet. Conventional channel estimation methods firstly computer the least-square(LS)channel estimates at the subcarrier(pilot tones)transmitting the pilot symbols directly computed from the know training symbols. Then, MMSE weighting in the frequency domain is applied to reduce the LS estimation error. To further improve the accuracy of channel estimation , the temporal channel correlation will be explored in the proposed algorithm. We will use the ρ—LMS algorithm to construct an autorecursive(AR)model for the frequency coefficient at each pilot-tone. The conditional mean estimate of the path gain will then be obtained from the constructed AR gain model, and the AR model coefficient will finally be updated by the gain estimate. Simulation result show that the proposed novel algorithm by the frequency and temporal correlation of the channel response can reduce the channel estimation mean square error(MSE)by 5 dB at 25 dB SNR.en_US
dc.description.tableofcontents中文摘要……………………………………………………………………...Ⅰ 英文摘要……………………………………………………………………...Ⅱ 目錄…………………………………………………………………………...Ⅲ 表目次………………………………………………………………………...Ⅴ 圖目次………………………………………………………………………. Ⅶ 第一章 序論……………………………………………………………..1 1.1 前言…………………………………………………………………..1 1.2 研究動機……………………………………………………………..1 1.3 論文結構……………………………………………………………..2 第二章 OFDM系統基本架構………………………………………….3 2.1 OFDM系統演進與簡介……………………………………………...3 2.2 OFDM 系統模型……………………………………………………..4 2.3 無線通訊的通道模型………………………………………………..6 第三章 相關的通道估計演算法………………………………………..8 3.1 引領式的通道估計……………………………………………….….8 3.2 LS及LMMSE的通道估計演算法………………………………….10 3.1.1 LS通道估計演算法…………………………………………….10 3.2.2 LMMSE通道估計演算法……………………………………....12 3.3通道內插的計算方式………………………………………………..13 3.4適應性的通道預測演算法…………………………………………..16 3.4.1 LMS(Least Mean Square)……………………………………18 3.4.2 RLS(Recursive Least Square)………………………………..19 3.4.3 ρ—LMS…………………………………………………...…...19 第四章 一種創新的利用時域相關性之通道估計技術………………23 4.1結合時間及頻率相關性的改良式通道估計技術…………………..23 4.2系統模擬的參數設定………………………………………………..23 4.3時變通道的模擬與分析……………………………………………..24 4.4改良式MMSE通道估計演算法之系統模擬結果………………….26 4.4.1未經過內插技術的系統模擬結果……………………………...28 4.4.2加入內插技術的系統模擬結果………………………………...30 第五章 結論……………………………………………………………36 參考文獻………………………………………………………………..37zh_TW
dc.language.isoen_USzh_TW
dc.publisher電機工程學系所zh_TW
dc.relation.urihttp://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-2908200715253000en_US
dc.subjectOFDMen_US
dc.subject通道估計zh_TW
dc.subjectChannel Estimationen_US
dc.subjectTemporla-correlationen_US
dc.subject時域相關性zh_TW
dc.title利用時間相關性處理之改良式OFDM系統通道估計演算法zh_TW
dc.titleImprovement of Pilot-Based Channel Estimation for OFDM Systems using temporal-correlation Processingen_US
dc.typeThesis and Dissertationzh_TW
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.languageiso639-1en_US-
item.openairetypeThesis and Dissertation-
item.grantfulltextnone-
item.fulltextno fulltext-
item.cerifentitytypePublications-
Appears in Collections:電機工程學系所
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