臺灣博碩士論文加值系統

在通訊鏈結上，由於都卜勒效應和傳送端與接收端震盪器頻率的不一致，頻率偏移與相位雜訊是不可避免的。通常相位雜訊常伴隨著時序誤差同時發生，所以這些誤差在接收端時應被補償。
為了解決這些問題，我們提出自我建置模糊類神經網路決策回授等化器(SCFNN DFE)一個低複雜度的調適性非線性等化器。它包含架構和參數學習階段，以訓練SCFNN DFE。而前饋輸入向量集合的分類，與梯度坡降法皆被用在此線上學習演算法中。
模擬顯示我們提出的設計能夠改善傳統決策回授等化器在頻率偏移、相位雜訊和時序誤差所造成的估測錯誤。

In communication links, a frequency offset due to Doppler effect, and a phase noise due to distorted transmission environment and imperfect oscillators exist. Phase noises usually accompanie the problem of timing error. These errors need to be compensated at the receiver to avoid a serious degradation.
To solve three difficulties, we propose a self-constructing fuzzy neural network-based decision feedback equalizer (SCFNN DFE) with a online learning algorithm containing the structure and parameter learning phases. Both the feedforward input vector classification and a gradient-descent method are for the learning algorithm.
Simulations show that the proposed SCFNN DFE improves the traditional DFE in the presence of estimation errors caused by frequency offset, phase noise and timing error.

摘 要......................................i
Abstract......................................ii
目 錄......................................iv
圖 目 錄......................................vi
表 目 錄......................................viii
第一章 緒論..................................1
1-1 前言 .....................................1
1-2 調適性等化器.............................3
1-3 自我建置調適等化器.......................5
1-4 本篇論文組織.............................7
第二章 模糊類神經網路........................8
2-1 類神經網路...............................8
2-2 決策回授等化器...........................10
2-3 模糊系統.................................12
2-4 模糊類神經網路...........................15
2-5 模糊類神經網路等化器.....................18
第三章 學習演算法 ............................21
3-1 自我建置模糊類神經網路決策回授等化器.....21
3-2 自我建置學習演算法.......................25
3-2-1 架構學習演算法.....................26
3-2-2 參數學習演算法.....................30
第四章 模擬結果與分析........................35
4-1 非線性失真通道模擬.......................35
4-1-1 位元錯誤率：.......................38
4-1-2 複雜度：...........................40
4-2 頻率偏移、相位雜訊環境模擬...............41
4-2-1 位元錯誤率：.......................44
4-2-2 複雜度：...........................45
4-3 時序誤差、相位雜訊環境模擬...............47
4-3-1 位元錯誤率：.......................50
4-3-2 複雜度：...........................51
第五章 結論..................................53
參考文獻......................................54

[1]T.S.Rappaport,“Wireless communication: principles and practice ( Edition),”Prentice Hall, pp. 355-414, 2002
[2]蘇木春, 張孝德,“機器學習:類神經網路、模糊系統以及基因演算法則,二版,全華科技圖書股份有限公司,臺北市,民國95年
[3]J.G.Proakis,“Digital Communication. Englewood Cliffs,”NJ: Prentice-Hall,1988
[4]J. S. R. Jang, C. T. Sun, and E. Mizutani,“Neuro-fuzzy and soft computing - a computational approach to learning and machine intelligence, Prentice Hall,”pp. 516-523, 1997
[5]W. D. Weng, R. C. Lin, and C. T. Hsueh,“The design of an SCFNN based nonlinear channel equalizer,”J. Inf. Sci. Eng,21,695-709,2005
[6]S. Siu, G. J. Gibson, and C. F. N. Cowan,“Decision feedback equalization using neural network structures and performance comparison with standard architecture,”IEE Proc,137,221-225,1990
[7]S. S. Yang, C. L. Ho, and C. M. Lee,“HBP: improvement in BP algorithm for an adaptive MLP decision feedback equalizer,”IEEE Trans. Circuits Syst. II-Express Briefs, 53, 240-244, 2006
[8]S. Siu, S. S. Yang, C. M. Lee and C. L. Ho,“Improving the back-propagation algorithm using evolutionary strategy,”IEEE Trans. Circuits Syst. II-Express Briefs, 54,171-175,2007
[9]S.Chen, B. Mulgrew, and P. M. Grant,“A clustering technique for digital communications channel equalization using radial basis function networks,” IEEE Trans. Neural Netw,4,570-579,1993
[10]S.Chen, B. Mulgrew, and S. McLaughlin,“Adaptive Bayesian equalizer with decision feedback,”IEEE Trans. Signal Process., 41, 2918-2927, 1993
[11]J. Lee, C. Beach, and N. Tepedelenlioglu,“A practical radial basis function equalizer,”IEEE Trans. Neural Netw, 10,450-455,1999
[12]J. S. R. Jang, C. T. Sun, and E. Mizutani,“Neuro-fuzzy and soft computing - a computational approach to learning and machine intelligence,”Prentice Hall, pp. 516-523,1997
[13]S. Siu, C.-L. Ho and C.-M Lee,“TSK-based decision feedback equalizer using an evolutionary algorithm applied to QAM communication system,”IEEE Trans.Circuit Syst.II-Express Briefs, Vol.52, no.9,pp.596-600, Sept.2005
[14]E. F. Harrington,“A BPSK decision-feedback equalization method robust to phase and timing errors,” IEEE Signal Process. Lett., 12, 313-316, 2005
[15]C.-M. Lee, S.-S Yang, and C.-L.Ho,“Modified back-propagation algorithm applied to decision-feedback equalisation, ”IEE Proc.-Vis.Image Signal Process.,Vol153, No.6,pp.805-809,December 2006
[16]A. Bateman,“Digital communications: design for the real world,”Addison Wesley, pp. 118-125, 1999