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dc.contributor.authorJheng, Kai-Wunen_US
dc.identifier.citation[1] 陳蠡, “應用於高階正交振幅調變纜線通信之整合載波回復與快速多階模數盲蔽式等化器設計與FPGA實作”, 國立中興大學論文, 民國96年6月 [2] 徐宛寧, “應用於高階QAM通信系統之載波回復器與多模數盲蔽式等化器協同設計與分析”, 國立中興大學論文, 民國99年7月 [3] 白政達, “高階QAM通信系統的盲蔽式等化與載波恢復協同設計與模擬”, 國立中興大學論文, 民國97年六月 [4] 李偉, “可應用於高階QAM調變系統之混合成本函數盲蔽式等化器設計與FPGA實作”, 國立中興大學論文, 民國94年六月 [5] K.N. Oh, and Y.O. Chin, “New Blind Equalization Techniques Based on Constant Modulus Algorithm”, IEEE Conference Global Telecommunications, vol. 2, pp 865-869, Nov 1995. [6] C.R. Johnson, JR., P. Schniter, “Blind Equalization Using the Constant Modulus Criterion: A Review”, Proceedings of the IEEE, vol. 86, pp. 1927-1950, Oct 1998. [7] T. Kurakake, N. Nakamura, and K. Oyamada, “A Blind 1024-QAM Demodulator for Cable Television,” Int. Zurich Seminar on Communications (IZS), pp. 18-20, Feb 2004. [8] E. Agrell, J. Lassing, and T. Ottosson, “Gray Coding for Multilevel Constellations in Gaussian Noise”, IEEE Transaction on Information Theory, vol 53,no. 1, pp 224-235, Jan 2007. [9] D.Godard, ”Self-Recovering Equalization and Carrier Tracking in Two-Dimensional Data Communication Systems”, IEEE Transactions on Communications, vol. 28, pp. 1867-1875, Nov 1980. [10] J. Yang, J.J Werner, and G.A. Dumont, “The Multimodulus Blind Equalization Algorithm”, International Conf. Digital Signal Processing, vol 1, pp 127-130, Jul 1997. [11] J. Yang, J.J Werner, and G.A Dumont, “The Multimodulus Blind Equalization and Its Generalized Algorithms”, IEEE journal in communications, vol.20, No. 5,pp. 997-1014, Jun 2002. [12] R. W. Lucky, “Techniques for adaptive equalization of digital communications systems”, Bell system Technical Journal 45,pp. 255-286, Feb1966. [13] C.P. Fan, W.H. Liang, W. Lee., “Fast Blind Equalization with Two-Stage Single/Multilevel Modulus and DD Algorithm for High Order QAM Cable Systems”, IEEE International Symposium on Circuits and Systems, Seattle, USA, May 2008. [14] John G. Proakis, Digital Communication, Fifth edition, McGraw-Hill, 2008. [15] E. Abdel-Raheem, "Blind Adaptive Equalization with Variable Step Size", ICICT ''06,Cairo, Egypt, pp.91-100, Dec.2006. [16] John G. Proakis and Masoud Salehi, “Contemporary Communication Systems using MATLAB”, BookWare Companion Series, 2000. [17] D.R. Stephens, “Phase-Locked Loops for Wirelss Communications Digital, Analog and Optical Implementations Second Edition”, Kluwer Academic Publishers, 2002 [18] M. Rupp and A.H. Sayed,“A time-domain feedback analysis of filtered-error adaptive gradient algorithms”, IEEE Trans. on Signal Processing, vol. 44, no.6, pp.1428-1439, June 1996 [19] J. Mai and A.H. Sayed ,“A feedback approach to the steady-state performance of fractionally spaced blind adaptive equalizers”, IEEE Trans. on Signal Processing, vol. 48, no.1, pp.80 – 91, Jan. 2000. [20] B. Lin, R. He, X. Wang, and B. Wang, “Excess MSE analysis of the concurrent constant modulus algorithm and soft decision-directed scheme for blind equalization”, IET Signal Processing, vol. 2, no.2, pp.147 – 155, June 2008.en_US
dc.description.abstract在數位通訊系統中,接收端必存在一負責消除通道效應干擾的等化器,隨著通道不同,在通訊系統運作前必須針對通道校正等化器係數。在本篇論文中,將介紹能自行校正的等化器做為研究對象,此種具自我校正回復傳輸訊號之等化器,統稱為盲蔽式等化器,主要被應用在如有線數位電視,以節省等化器訓練過程中所損耗的頻寬。 首先,我們將會介紹數個通道非理想效應,分別為熱雜訊干擾、非線性失真、多路徑效應、符元間干擾效應、載波飄移,以便認識等化器所必須容忍、校正的環境。接著對基頻通訊領域所對應的系統架構做出定義,包含了收發端與通道模型。 有了通道與對應收發端的模型後,便能開始對本篇論文主題—盲蔽式等化器做探討,典型的盲蔽式演算法為固定模數演算法(CMA),加入星座圖分區概念後成為多階模數演算法(MLMA),能加快收斂速度,同時降低穩態誤差。另一個盲蔽式演算法分支為多模數演算法(MMA)亦稱為改良之固定模數演算法(MCMA),已被證明其收斂速度比固定模數演算法快,且擁有額外的相位回復能力與抗符元間干擾(ISI)能力,但無法搭配傳統載波回復架構進行頻率飄移校正。在本研究中成功的將多模數演算法(MMA)與載波回復電路結合,並推展到其進化之廣義多模數演算法(GMMA)。在64QAM、256QAM、1024QAM下完成載波回復電路整合模擬,此三種調變環境,在10M符元速率環境下皆可追蹤至±100kHz的載波飄移。此一突破使多模數演算法能在實際通訊系統中被應用。為了能讓盲蔽式等化器能有最佳通道效應干擾回復效果,會在盲蔽式演算法收斂至穩態後,將每個回復之浮元視為訓練訊號,使用更精確但無法容忍錯誤訓練訊號的決策運算(DD)與增強型決策運算(EDD)演算法進行校正,以達到與使用訓練符元通訊系統相同效能,此篇論文中亦是使用此種作法,故對於決策運算相關演算法也會於論文中介紹。 決策迴授等化器(DFE)是降低符元間干擾的有效方法,我們對於串接多級迴授等化器之實現方式做探討,模擬其效能表現後,發現能加快等化器收斂速度,對此我們做出了定性解釋與詳細的穩態誤差表現分析。zh_TW
dc.description.abstractIn digital communication systems, baseband receivers must eliminate multi-path channel effects by equalizers. Before communication links are established, there is a procedure to calibrate equalizer`s coefficients. The applied methodology will focus on blind equalizers. The blind equalizer does not use any training symbols to adjust equalizer’s coefficients. Consequently, it saves more bandwidths compared with training based equalizers. Today, the major applications of blind equalizers are wired digital television and cable communication systems on hybrid fiber-coaxial (HFC) networks. In this thesis, firstly, couples of channel’s non ideal effects, which include thermal noises, nonlinear distortions, multi-path effects, inter-symbol interferences, carrier offsets, are introduced for background knowledge. Next, we will discuss what blind equalizers deal with. After that, we define the system architecture includes the transmitter model, the receiver model, and the channel model in baseband communications. After introducing about the system model, we focus on discussing blind equalizers. The classical algorithm for blind equalizations is the CMA (Constant Modulus Algorithm), and we extend the CMA technique to the MLMA (Multi-level Modulus Algorithm) technique. The MLMA can accelerate the convergence speed, compared with the CMA method. Meanwhile, it achieves less MSE than CMA. There exists another algorithm, which is similar to CMA, called MMA (Multi-Modulus Algorithm) or MCMA (Modified Constant Modulus Algorithm). The MCMA method has quicker convergence time than the CMA method, and it performs additional phase recovery ability and also resists ISI (inter-symbol interference) effect. However, the MCMA method cannot recover carrier offsets with the traditional carrier recovery scheme. Our research combines the MCMA based equalizer with new carrier recovery architecture and progresses to the GMMA method successfully. The simulation results show that our architecture can converge under 64QAM, 256QAM, and 1024QAM modulations with ±100kHz frequency offsets under 10MHz symbol rates. The breakthrough let the multi-modulus algorithm be applied to practical cable communication systems. To reach less MSE, when the blind equalization goes to the steady state, our equalization scheme will turn on the EDD (Enhanced Decision Directed) method. Thus, the proposed two-stage mechanism supports the blind equalization to achieve low MSE, which is the same as that calibrated by training processes. The DFE (decision feedback equalizer) is an effective scheme to deal with inter-symbol interferences. In this thesis, we introduce the cascade of multi-stage DFEs. The simulation results show that a 2-stage DFE requires less convergence time, compared with a single stage DFE. We also propose some mathematical and behavior analyses for the scheme of 2-stage DFEs.en_US
dc.description.tableofcontents中文摘要 ii Abstract iii 第一章 簡介 1 1.1 混合光纖同軸網路 1 1.2 多路徑效應 3 1.3 白高斯雜訊 3 1.4 相位誤差與載波飄移效應 5 第二章 盲蔽式等化器相關演算法介紹 7 2.1 通訊系統架構 7 2.2 固定模數演算法(CMA) 9 2.3 改良之固定模數演算法(MCMA) 10 2.4 多階模數演算法(MLMA) 13 2.5 廣義多模數演算法(GMMA) 17 2.6 決策運算(DD)與增強型決策運算(EDD) 20 2.7 可變步階調整(VSS) 22 2.8 廣義多模數演算法(GMMA)與增強型決策運算(EDD) 之結合 23 2.9 載波回復電路 27 第三章 多級決策迴授等化器(MULTISTAGE DECISION FEEDBACK EQUALIZER)分析與模擬 30 3.1 系統架構 30 3.2 多級決策迴授電路模擬 33 3.3 多級決策迴授電路穩態分析 35 第四章 等化器(EQUALIZER)與載波回復電路(CR)整合分析與模擬 37 4.1 固定模數演算法(CMA)與載波回復電路(CR)整合 37 4.2 多模數演算法(MMA)與載波回復電路之整合 39 4.3 64QAM 調變下載波回復整合模擬 43 4.4 256QAM 調變下載波回復整合模擬 48 4.5 1024QAM 調變下載波回復整合模擬 53 第五章 結論與未來工作 58 參考文獻 59zh_TW
dc.subjectBlind Equalizeren_US
dc.subjectMulti-Modulus Algorithmen_US
dc.subjectCarrier Offset Effecten_US
dc.subjectCarry Recoveryen_US
dc.titleDesign and Simulation of Improved Carrier Recovery Scheme and Multi-Modulus Decision-Feedback Equalizer for HFC Communicationsen_US
dc.typeThesis and Dissertationzh_TW
item.openairetypeThesis and Dissertation-
item.fulltextno fulltext-
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