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

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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 )

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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
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指導教授

賀嘉律(Chia-Lu Ho)

審核日期

2003-7-14

推文