研究非線性功率放大器的線性化預失真技術

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姓名

羅彥翔(Yen-Hsiang Lo) 查詢紙本館藏

畢業系所

通訊工程學系

論文名稱

研究非線性功率放大器的線性化預失真技術
(Study On Linearization Methods For Predistortion Of Nonlinear Power Amplifiers)

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摘要(中)

由於正交分頻多工(Orthogonal Frequency Division Multiplexing, OFDM)訊號具有較高的峰值對均值功率比(Peak-to-Average Power Ratio, PAPR)，容易受到功率放大器非線性的影響，而產生訊號的失真，導致系統效能降低。在過去的文獻中，使用預失真技術用於補償非線性功率放大器造成的失真為主要的研究方向。在本篇論文以有記憶性多項式模型當作功率放大器，並利用有記憶性多項式作為預失真器的模型，並利用有記憶性多項式作為預失真器的模型，提出NM單純型搜索法 (Nelder and Mead Simplex Search Method)直接搜索預失真參數，以及使用高斯-牛頓線性化法(Gaussian-Newton method)推導出有記憶性多項式模型下的演算法，最後提出最大期望値法(Expectation Maximization)結合NM單純型搜索法，並且分別利用直接學習架構(Direct Learning Architecture,DLA)以及間接學習架構(Indirect Learning Architecture,ILA)找到預失真的參數，最後本篇論文比較補償非線性功率放大器演算法的效率。

摘要(英)

The characteristic of high peak-to-average power ratio (PAPR) of orthogonal frequency division multiplexing (OFDM) signals is well known to seriously degrade system performance. The predistortion (PD) technique for compensating the nonlinear power amplifiers (PAs) has become a main approach in the literature. In this paper, we consider a memory polynomial model for the PAs. Taking into account the direct learning and indirect learning structures for the PD, we study some algorithms for the PD coefficients, including the expectation maximum (EM) algorithm, Gaussian-Newton linearization method, and the Nelder-Mead simplex search method. In this thesis, the algorithm efficiency and computational complexity are compared by applying those methods for the PA compensation problem.

關鍵字(中)

★ 正交分頻多工
★ 直接學習架構
★ 間接學習架構
★ 預失真技術
★ 內爾德 - 米德
★ 最大期望值
★ 高斯-牛頓

關鍵字(英)

★ OFDM
★ DLA
★ ILA
★ predistortion
★ Nelder-Mead
★ expectation maximization
★ Gaussian-Newton

論文目次

中文摘要 . . . . . . . . . . . . . . . . . . . . . . . . i
英文摘要 . . . . . . . . . . . . . . . . . . . . . . . iii
目錄 . . . . . . . . . . . . . . . . . . . . . . . . . .i
圖目錄 . . . . . . . . . . . . . . . . . . . . . . . . ii
表目錄 . . . . . . . . . . . . . . . . . . . . . . . .iii
第 1 章序論 . . . . . . . . . . . . . . . . . . . . . . 1
1.1 前言 . . . . . . . . . . . . . . . . . . . . . . . .1
1.2 章節架構 . . . . . . . . . . . . . . . . . . . . . .5
第 2 章系統模型 . . . . . . . . . . . . . . . . . . . . 7
2.1 傳輸訊號模型 . . . . . . . . . . . . . . . . . . . . 7
2.2 功率放大器 (Power Amplifier) . . . . . . . . . . . .10
2.2.1 無記憶性多項式模型 (Memoryless Polynomial)) . . . 11
2.2.2 有記憶性多項式模型 (Memory Polynomial)) . . . . . 11
2.3 預失真線性化技術 (Predistortion linearization) . . .12
第 3 章預失真線性化演算法 . . . . . . . . . . . . . . . .16
3.1 NLMS . . . . . . . . . . . . . . . . . . . . . . . 17
3.1.1 DLA 使用 NLMS 演算法更新係數步驟 . . . . . . . . . 18
3.1.2 ILA 使用 NLMS 演算法更新係數步驟 . . . . . . . . . 19
3.2 RLS . . . . . . . . . . . . . . . . . . . . . . . .21
3.2.1 DLA 使用 RLS 演算法更新係數步驟如下 . . . . . . . .21
3.2.2 ILA 使用 RLS 演算法更新係數步驟如下 . . . . . . . .23
3.3 單純型搜索法 (Nelder and Mead Simplex Search Method)24
3.4 最大期望演算法 . . . . . . . . . . . . . . . . . . .29
3.4.1 期望步驟 (E-step) . . . . . . . . . . . . . . . .31
3.4.2 最大化步驟 (M-step) . . . . . . . . . . . . . . .33
3.5 高斯-牛頓法 (Gauss-Newton) . . . . . . . . . . . . .36
3.5.1 高斯-牛頓法利用 DLA . . . . . . . . . . . . . . . 36
3.5.2 高斯-牛頓法利用 ILA . . . . . . . . . . . . . . . 39
第 4 章系統模擬與結果分析 . . . . . . . . . . . . . . . .41
4.1 星座點比較 . . . . . . . . . . . . . . . . . . . . .43
4.2 誤差向量幅度 . . . . . . . . . . . . . . . . . . . .46
4.3 演算法收斂曲線比較 . . . . . . . . . . . . . . . . .48
4.4 功率頻譜密度 . . . . . . . . . . . . . . . . . . . .56
第 5 章結論 . . . . . . . . . . . . . . . . . . . . . . 60
附錄 A：Gauss-Newton 微分推導. . . . . . . . . . . . . .61
參考文獻 . . . . . . . . . . . . . . . . . . . . . . . .64

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指導教授

張大中

審核日期

2016-11-1

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