博碩士論文 90521096 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:8 、訪客IP:18.208.187.169
姓名 吳啟東(Chi-Dong Wu)  查詢紙本館藏   畢業系所 電機工程學系
論文名稱 以調適性類神經網路系統實現預先失真器補償 RF 功率放大器之非線性效應
(Compensating the Nonlinear Effect of RF Power Amplifiers with Neural Network based Adaptive Predistortion )
相關論文
★ 進化演算法應用在數位濾波器之最佳化設計★ 進化演算法之動態分析及應用於數位濾波器之設計
★ WDM同步光纖網路加入/取出多工器效應之評估★ PN碼對多重路徑的估測
★ 多層感知等化器-使用進化演算法★ Lp Norm 倒傳遞演算法使用在調適性濾波器
★ 利用進化演算法在多層感知機結構上之判別回授等化器★ 模糊類神經網路結合進化演算法運用在基頻通道等化器上
★ 使用進化演算法的模糊化類神經網路等化器★ 新式的電信網路主參考信號源
★ 應用進化演算法於類神經網路之判別回授 等化器與探討各參數對performance的影響★ 進化演算法結合多層感知機架構運用在4-QAM決策迴授等化器上
★ 進化演算法應用在多層感知迴授等化 器上之效能分析★ 複數訊號多層感知決策回授等化器-使用進化演算法
★ 頻移相位同調光纖通信系統的效能分析★ 多層感知器對輸入與權值誤差的敏感度分析及倒傳遞(BP)演算法與進化策略(ES)演算法的改善
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 ( 永不開放)
摘要(中) 論文提要及內容:
對於高資料傳輸率無線通訊系統而言,在線性調變技術中擁有好的頻譜效率是相當吸引人的。然而,此類系統的浮動波包(fluctuating envelopes) 卻結合了來自高功率RF放大器的非線性現象,以致於造成QAM調變訊號的扭曲效應(warping effect),進而嚴重影響傳輸品質。為了有效消除傳輸端中的warping effect,本論文將使用於基頻操作的data predistorter作為所需的補償器。其中,我們使用多層類神經網路系統來當作predistorter中所使用的nonlinear filter,它將被訓練成為高功率放大器響應的反函數,並且進行基頻資料預先扭曲的非線性補償。為了實現此inverse filter,本論文中使用了多層感知器架構配合複數倒傳遞演算法 (Complex Backpropagation,CBP)類神經網路,並與最小均方演算法(Least Mean Square,LMS)與實數倒傳遞演算法(Real Backpropagation,RBP)類神經網路的效能比較。除了各種演算法的介紹外,為了論文的完整性及一致性,本論文將從基本的類神經網路來開始討論,最後再將電腦模擬的結果附上以說明各種演算法的性能比較。
摘要(英) Abstract of thesis:
The good spectral efficiency of linear modulation techniques makes them attractive for use in high date rate digital radio system. Nevertheless, the fluctuating envelopes of such systems combined with the nonlinear nature of the high power RF amplifiers commonly. The warping effect caused by the high power amplifier will seriously degrade the transmission quality of QAM modulated signals. In order to suppress warping effect, one possibility is to use data predistorter operating at baseband as a compensator. In this case, we present a preliminary implementation of a data predistortion system using a multilayer perceptron neural network which forms an adaptive nonlinear filter whose response approximates the inverse function of the HPA nonlinearity. The neural network utilized in this work is a multilayer perceptron using Complex Backpropagation(CBP) algorithm to improve the performance of Least Mean Square(LMS ) algorithm and Real Backpropagation (RBP) algorithm.
關鍵字(中) ★ 類神經網路
★ 功率放大器
★ 預先失真器
關鍵字(英) ★ Power Amplifiers
★ Neural Networks
★ Predistortion
論文目次 目錄
圖目錄、表目錄 …………………..…………………………………… Ⅲ
第一章 緒論
1.1研究目的…………….……..……………………………….. 1
1.2研究動機…….……..…………..…………………………… 2
1.3各章節提要……………….……..………………………….. 2
第二章 類神經網路架構
2.1類神經網路介紹.………………….…………………….….. 4
2.1.1生物神經元的結構………………….…………….…...7
2.1.2類神經元的模型……………………………….………9
2.2類神經網路架構………………………………..…….…….. 12
2.3多層感知器……………………………………….………….15
2.4類神經網路建構步驟………………….………….…………18
2.5類神經網路優缺點………………….…………….…………19
2.6類神經網路的推廣能力…………….…………….…………21
第三章 最小均方演算法及倒傳遞演算法
3.1最小均方演算法……………………………..……….……. 24
3.2倒傳遞類神經網路…………..…………….………….……. 27
3.3實數域中的倒傳遞演算法………………….…….….…….. 28
3.4複數域中的倒傳遞演算法………………….….….……….. 33
3.5倒傳遞類神經網路訓練之限制..…………….…………..… 39
3.6倒傳遞類神經網路之注意事項………..………….……….. 41
第四章 類神經網路預先失真器
4.1高功率放大器介紹………………………………...………..44
4.2信號及放大器模型………………………………...………..47
4.3高功率放大器的非線性失真……………………………….49
4.4高功率放大器線性化技術……………………….…………..54
4.4.1反減補償法……………………………………………55
4.4.2回授技術………………………………………………57
4.4.3順授技術………………………………………………59
4.4.4預先失真技術…………………………………………60
4.4.5 Postdistortion技術.………………………………63
4.5類神經網路預先失真器……………………………………...64
4.5.1以間接學習法實現調適性預先失真器………………65
第五章 模擬結果
5.1最小均方演算法的模擬結果………………………………..70
5.1.1 LMS的模擬―均方誤差...............................................70
5.1.2 LMS的模擬―位元錯誤率…………………………...72
5.2實數訊號倒傳遞演算法的模擬結果……………….………..73
5.2.1 RBP的模擬―均方誤差………………………….…...73
5.2.2 RBP的模擬―位元錯誤率……………………………75
5.3複數訊號倒傳遞演算法的模擬結果……………….………..76
5.3.1 CBP的模擬―均方誤差………………………………76
5.3.2 CBP的模擬―位元錯誤率……………………………78
第六章 結論……………………………………….…………………….... 81
參考文獻………………………………………….……………………….. 82
圖目錄、表目錄
圖2.1 系統之輸入與輸出關係...………………………………………... 4
圖2.2 以類神經取代系統之輸入與輸出關係……………..…...………. 5
圖2.3 生物神經細胞模型………………………………………….……. 7
圖2.4 人工神經元模型…………………………………………….……. 9
圖2.5 單一輸出神經元類神經網路………………………….…….……. 11
圖2.6 常見之類神經網路架構……………..……….…………...….…… 13
圖2.7 (2,7,2)兩層感知器類神經網路架構………..……..…..…….…….. 14
圖2.8 感知器之架構方塊……………..…………..……………..……….15
圖2.9 Sigmoid Function…………..………………………….….………. .16
圖2.10多層感知器的架構……………….…………………….….…….. 17
圖2.11類神經網路推廣能力良好之輸入/輸出關係圖………...…...…. 21
圖2.12類神經網路過度訓練之輸入/輸出關係圖………..……………. 21
圖3.1 倒傳遞演算法網路的初始值設定……………………………….. 40
圖4.1 功率放大器輸入電壓與輸出電流波形………………………….. 45
圖4.2 波包的AM/AM與AM/PM非線性失真…..……………………..48
圖4.3 HPA的非線性效應………………………….……………………..50
圖4.4 理想功率放大器AM/AM轉換曲線………………………….…..51
圖4.5 理想功率放大器AM/PM轉換曲線………………………….…...52
圖4.6 非線性功率放大器AM/AM轉換曲線……………………….…..52
圖4.7 非線性功率放大器AM/PM轉換曲線…………………….…..….53
圖4.8 功率放大器之input/output back-off對照圖…………………...…56
圖4.9 一般回授系統………………………………….…………………..57
圖4.10 回授技術應用在放大器系統失真………………..………….......58
圖4.11 基本順授技術示意圖…………………………………..………...59
圖4.12 預先失真器補償流程圖………………………………..…….…..60
圖4.13 RF amplifier 與predistorter增益補償曲線……………………...61
圖4.14 Postdistortion技術補償流程圖………………………...….……...63
圖4.15 類神經網路預先失真器之間接學習架構………………...…......66
圖5.1 理想16QAM訊號之星座圖……………………………...……...69
圖5.2 16QAM訊號經HPA非線性扭曲後之星座圖………...…...…...69
圖5.3 LMS演算法補償HPA非線性之MSE性能圖……………...….71
圖5.4 LMS演算法補償HPA非線性之BER性能圖………...……..…72
圖5.5 RBP演算法補償HPA非線性之MSE性能圖……………….…74
圖5.6 RBP演算法補償HPA非線性之BER性能圖…………….….....75
圖5.7 CBP演算法補償HPA非線性之MSE性能圖………….………77
圖5.8 CBP演算法補償HPA非線性之BER性能圖…………….…….78
圖5.9 各種演算法之MSE性能比較圖……………………..….………79
圖5.10 各種演算法之BER性能比較圖……………………..…….…….80
表4.1 功率放大器基本特性………………………….…………………...44
表4.2 功率放大器的分類………….……………………………………...46
參考文獻 參考文獻
(Reference)
[1] Watkins, B.E., North, R., and Tummala, M., "Neural network based adaptive predistortion for the linearization of nonlinear RF amplifiers", Proceedings of MILCOM '96, IEEE Military Commun. Conf., 1995.
[2] C. Eun and E. J. Powers, "A new Volterra predistorter based on the indirectlearning architecture", IEEE Trans. Signal Processing, vol. 45, no. 1, pp.223-227, Jan. 1997.
[3] A. Bernardini and S. De Fina, "Application of neural waveform predistortion to experimental TWT data",IEEE 6th Mediterranean Elec-trotechnical Conf., pp. 468-71, vol. 1, 1991.
[4] Bruce E. Watkins and Richard North, "Predistortion of nonlinear amplifiers using neural networks", Proceedings of MILCOM '96, IEEE Military Commun. Conf., 1996.
[5] Georges Karam and Hikmet Sari, "Data predistortion techniques using intersymbol interpolation",IEEE Trans. Commun., vol. 38, no. 10, pp. 1716- 1723, Oct. 1990.
[6] Y. Levy, G. Karam, and H. Sari, "Adaptation of a digital predistortion technique based on intersymbol interpolation", Global Telecommuni- cations Conf. 1995, GLOBECOM '95 IEEE.
[7] M. Faulkner and M. Johansson, "Adaptive linearization using predistortion experimental results", IEEE Trans. Veh. Technol., vol. 43, no. 2, pp. 323-332, May 1994.
[8] A. Bernardini and S. De Fina, "Analysis of different optimization criteria for IF predistortion in digital radio links with nonlinear amplifiers" ,IEEE Trans. Commun., vol. 45, no. 4, pp. 421-428, Apr. 1997.
[9] A. N. D'Andrea, V. Lottici and R. Reggiannini, "RF power amplifier linearization through amplitude and phase predistortion", IEEE Trans. Commun., vol. 44, no. 11, pp. 1477-1484, Nov. 1996.
[10] M. Ibnkahla, J. Sombrin, F. Castanie and N. J. Bershad, "Neural networks for modeling nonlinear memoryless communication channels", IEEE Trans. Commun., vol. 45, no. 7, pp. 768-771, July 1997.
[11] B. Hashem and M. S. EI-Hennawey, "Performance of theπ/4-DQPSK, GMSK,and QAM modulation schemes in mobile radio with multipath fading and nonlinearities", IEEE Trans. Veh. Technol., vol. 46, no. 2, pp. 390-395, May 1997.
[12] G. Karam and H. Sari, "A data predistortion technique with memory for QAM radio systems", IEEE Trans. Commun., vol. 39, no. 2, Feb. 1991.
[13] G. Karam and H. Sari, "Analysis of predistortion, equalization, and ISI cancellation techniques in digital radio systems with nonlinear transmit amplifier", IEEE trans. Commun., vol. 37, pp.1245-1253, Dec. 1989.
[14] D. Psaltis, A. Sideris, and A. A. Yamamura, "A multilayered neural network controller",1987 International Conference on Neural Net-works, June 21-24, 1987.
[15] A. A. M. Saleh, "Frequency-independent and frequency-dependent nonlinear models of TWT amplifiers", IEEE Trans. Commun., vol. COM-29, pp. 1715-1720, Nov. 1981.
[16] C.H. Chang, S. Siu, and C.H. Wei, “Decision feedback equalization using complex backpropagation algorithm”, in Proc. of IEEE International Symposium on Circuits and Systems, Hong Kong, pp. 589-592, June 1997.
[17] C.H. Chang, S. Siu, and C.H. Wei, “Complex backpropagation decision feedback equalizer with decision using neural nets”, Journal of The Chinese Institute of Electrical Engineering, vol. 7, no. 1, pp. 63-69, 2000.
[18] N. Benvenuto, and F. Piazza, “On the complex backpropagation algorithm”, IEEE Trans. Signal Processing, vol. 40, no. 4, pp. 967-969, 1992.
[19] H. Leung, and S. Haykin, “The complex backpropagation algorithm”, IEEE Trans. Signal Processing, vol. 39, no. 9, pp. 2101-2104, 1991.
[20] V.Soula & F. Gourgue, “Linearization of Power Amplifiers Modeling and Simulations”, Proceedings of IEEE Globecom, 1456-1461, 1994.
[21] R.D. Stewart & F. Tusubira, “Feedforward Linearisation of 950MHz Amplifiers”, IEE Proceedings, 347-350, October 1988.
[22] J. K. Cavers, “Amplifier Linearization Using a Digital Predistorter with Fast Adaptation and Low Memory Requirements”, IEEE Trans. On Vehicular Technology, 374-382, Novembor 1990.
[23] G. Cybenko, “Approximation by Superpositions of a Sigmoid Function”, Mathematics of Control, Signals, and Systems, 2, 303-314, 1989.
[24] S. Siu, G.J. Gibson and C.F.N. Cowan, “Decision feedback equalization using neural network structures and performance comparision with standard architecture”, IEE Proceedings, Vol. 137, No.4, August 1990.
[25] In-Seung Park & Edward J. Powers, “An Adaptive Predistorter for High Power Amplifiers”, IEEE Signals, Systems & Computers, vol. 1, pp. 8-12, Novembor 1997.
[26] Mohamed IBNKAHLA, “Neural Network Predistortion Technique for Digital Satellite Communications”, IEEE Acoustics, Speech, and Signal Processing, vol. 6, pp. 3506-3509, 2000.
[27] Nikos Naskas & Papananos, “Adaptive Baseband Predistorter for Radio Frequency Power Amplifiers Based on a Multiayer Perceptron”, IEEE Electronics, Circuits and Systems, vol. 3, pp. 1107-1110, 2002.
[28] S. Haykin, Adaptive Filter Theory. Prentice Hall, Englewood Cliffs, NJ, Fourth Edition, 2001.
[29] J. G. Proakis, Digital Communications. McGRAW-HILL International Editions, Fourth Edition, 2000.
[30] S. Hakin, Neural Networks: A Compreheneive Foundation, 2rd Edition, Prentice-Hall,Englewood Cliffs, NJ, 1999.
[31] Bernard Sklar, Digital communications, 2nd Edition, Prentice-Hall, 2001.
[32] James A. Freeman, Simulating neural networks with mathematica, Addison-Wesley, 1994.
[33] Yoh-Han Pao, Adaptive pattern recognition and neural networks, Addison-
Wesley, 1989.
[34] Peter B. Kenington, High-Linearity RF Amplifier Design, Artech House, 2000.
[35] 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, 全華科技圖書股份有限公司.
[36] 羅華強, 類神經網路-Matlab的應用,清蔚科技股份有限公司.
[37] 楊柏勳, 以可適性模糊系統補償高功率放大器之非線性效應, 私立元智大學電機工程研究所碩士論文, 1998.
[38] 申家玲, 802.11a傳輸端減少非線性效應之訊號預先處理, 私立元智大學電機工程研究所碩士論文, 2001.
[39] 陳逸樺, 以基頻預先補償實現2.4GHz CCK編碼調變之高線性發射機, 私立元智大學電機工程研究所碩士論文,2000.
[40] 王士鳴, IS-95 CDMA射頻傳收機模組及其功率放大器元件MMIC設計, 國立中山大學電機工程研究所碩士論文, 2000.
[41] 顏呈機, 2.4GHz ISM頻帶收發機射頻前端CMOS RFIC及使用二極體線性器CMOS PA之研製, 國立成功大學電機工程研究所博士論文, 2001.
[42] 楊盛松, 複數訊號多層感知決策回授等化器-使用進化演算法, 國立中央大
學電機工程研究所碩士論文, 2001.
[43] 張吉良, 利用進化演算法在多層感知機結構之判別回授等化器, 國立中央大學電機工程研究所碩士論文, 2000.
指導教授 賀嘉律(Chia-Lu Ho) 審核日期 2003-7-14
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明