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標題: 應用於混合光纖同軸網路通信之改良載波回復與多模數決策回授等化器協同設計與模擬
Design and Simulation of Improved Carrier Recovery Scheme and Multi-Modulus Decision-Feedback Equalizer for HFC Communications
作者: 鄭凱文
Jheng, Kai-Wun
關鍵字: 盲蔽式等化器;Blind Equalizer;多模數演算法;載波飄移效應;載波回復;Multi-Modulus Algorithm;Carrier Offset Effect;Carry Recovery
出版社: 電機工程學系所
引用: [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.

In 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.
其他識別: U0005-1408201223522400
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