臺灣博碩士論文加值系統

在傳統以定值模數為基礎(CMA-based)的盲蔽等化(Blind equalization)演算法中，存在著二個相當嚴重的缺點：緩慢的收斂速度以及在達到穩定狀態時有較大的平均平方誤差(Mean-squared error)。我們在這篇論文中提出一種新的演算法去改善盲蔽等化器的性能。我們所提出的演算法結合了Stop-and-Go方針、修定的定值模數演算法(MCMA)、CMA-RLS架構的優點，使得等化器不僅僅在收斂速度上能獲得相當大的改進，更能得到一個較低的穩定狀態的平均平方誤差。另外，我們把所提出的演算法在分數空間等化器[Fractionally Spaced Equalizer, (FSE)]中實現。由最後的電腦模擬結果可以知道這個演算法在較惡劣的通道中以及在較低的訊號雜訊比(SNR)都能夠有相當不錯的性能。

The conventional CMA-based algorithms exhibit slow convergence rate and high mean-squared error (MSE) in the steady state. In this thesis, we propose a new algorithm to improve the performance of the existing blind equalization algorithms. The proposed algorithm combines the advantages of the stop-and-go strategy, the modified constant modulus algorithm (MCMA) and the CMA-RLS algorithm. It not only can significantly accelerate the rate of convergence but also can improve the steady state MSE. Moreover, we combine the algorithm with the fractionally spaced equalizer (FSE) to further improve the performance especially in the severely distorted channels or low SNR environments. Computer simulations demonstrate that the proposed algorithm indeed achieves excellent performance in terms of rate of convergence and steady state MSE.

1. INTRODUCTION 1
1.1 The Baseband Model 3
1.2 The Channel Model 4
2. THE BLIND EQUALIZATION ALGORITHMS 8
2.1 The Constant Modulus Algorithm (CMA) 8
2.2 The Modified Constant Modulus Algorithm (MCMA) 11
2.3 The Stop-and-Go Strategy 13
2.3.1 The Stop-and-Go Decision-Directed (SG-DD) Algorithm 14
2.3.2 The Godard Stop-and-Go Decision-Directed (GSG-DD) Algorithm 16
2.4 The CMA-RLS Algorithm 16
2.5 Simulations 19
3. THE PROPOSED STOP-AND-GO DECISION-DIRECTED LEAST- SQUARES ALGORITHM 26
3.1 The Modified Godard Stop-and-Go Decision-Directed (MGSG-DD)
algorithm 26
3.2 The Stop-and-Go Decision-Directed Least-Squares Algorithm 28
3.3 The Fractionally Spaced Equalizers (FSE) 32
3.3.1 The Fractionally Spaced Equalizer Model 33
3.3.2 Mathematical Expressions of FSE 36
3.4 The Proposed Stop-and-Go Decision-Directed Least-Squares
Algorithm using FSE 39
3.5 Simulations 44
4. CONCLUSIONS 55
REFERENCE 56

[1] Y. Sato, “A method of self-recovering equalization for multilevel alplitude modulation system,” IEEE Trans. on Communication, vol. COM-23, pp.679-682, June 1975.
[2] D. N. Godard, “Self-recovery equalization and carrier tracking in two-dimensinal data communication system,” IEEE Trans. on Communication, vol. COM-28, pp.1867-1875, Nov. 1980.
[3] V. Weerackody and S. A. Kassam, “Dual-mode type algorithm for blind equalization,” IEEE Trans. on Communication, vol. COM-42, pp.22-28, Jan. 1994.
[4] J. R. Treichler, V. Wolff and C. R. Johnson, Jr., “Observed misconvergence in the constant modulus adaptive algorithm,” Proc. 25th Asilomar Conf. Signals, Syst., Computers, Pacific Grove, CA, pp.663-667, Nov. 1991.
[5] K. N. Oh and Y. O. Chin, “Modified constant modulus algorithm: blind equalization and carrier phase recovery algorithm,” IEEE International Conference on communication, vol. 1, pp.498 -502, Seattle, WA, June 1995.
[6] K. N. Oh and Y. O. Chin, “New blind equalization techniques based on constant modulus algorithm,” GLOBECOM '95., IEEE, vol. 2, pp.865 -869.
[7] G. Picchi and G. Prati, “Blind Equalization and Carrier Recovery Using a “Stop-and-Go” Decision-Directed Algorithm.” IEEE Trans. Commun., vol. COM-35, pp.877-887, Sept. 1987.
[8] D. Hatzinakos, “Blind equalization using stop-and-go adaptation rules,” Optical Engineering, Vol. 31, No. 6, pp.1181-1188, June 1992
[9] C. H. Tseng and C. B. Lin, “A Stop-and-Go Dual-Mode Algorithm for Blind Equalization,” GLOBECOM '96, vol.2, pp. 1427 -1431, 1996
[10] A. Benveniste, M. Goursat, and G. Ruget, “Robust identification of a nonminimum phase system: blind adjustment of a linear equalizer in data communications,” IEEE Trans. on Automatic Control, AC-25, pp. 385-399, June 1980.
[11] S. Haykin, Adaptive Filter Theory, 3rd ed., Prentice-Hall, Englewood Cliffs, N.J., 1996
[12] J. —T. Yuan and Y. C. Liao, “Performance enhancement of SISO blind equalization using decision-directed least-squares estimation,” 1st IEEE International Symposiums on Signal Processing and Information Technology, Cairo, Egypt, pp. 205-208 ,Dec. 2001.
[13] S. Haykin, Unsupervised Adaptive Filtering Vol. II: Blind Deconvolution, John Wiley & Sons, 2000.
[14] W. A. Gardner, “Exploitation of Spectral Redundancy in Cyclostationary Signals,” IEEE Signal Processing Magzine, pp.14-35, Apr. 1991
[15] C. R. Johnson, Jr., P. Schniter, T. J. Endres, J. D. Behm, D. R. Brown, and R. A. Casas, “Blind Equalization Using The Constant Modulus Criterion: A Review,” Proceedings of the IEEE, vol. 86, no.10, pp.1927-1950, Oct. 1998.
[16] Y. Li and Z. Ding, “Global Convergence of Fractionally Spaced Godard (CMA) Adaptive Equalizers,” IEEE Trans. on Signal Processing, vol.44, no.4, Apr. 1996
[17] Fijalkow I., F. Lopez de Victoria, and C. R. Johnson, Jr., “Adaptive fractionally spaced blind equalization,” Proc. IEEE Signal Processing Workshop, pp.257-260, Oct. 1994.
[18] J. —T. Yuan, “A Modified QRD for Smoothing and a QRD-LSL Smoothing Algorithm,” IEEE Trans. Signal Processing, vol. 47, no. 5, May 1999, pp. 1414-1420.