臺灣博碩士論文加值系統

由於功率放大器所具有的非線性效應常會為無線通訊系統帶來帶內失真及帶外放射等問題，其結果將分別為訊號帶來位元錯誤率的上升和鄰頻干擾等問題，也因而導致通訊系統效能的降低；正交分頻多工系統具有高頻寬效能及有效對抗多路徑干擾，也因此成為現今主要的無線通訊規格之一，但其與生俱來的高峰均功率比則會嚴重地影響到放大器的功率效能，為了能夠有效的解決這些問題，降低峰均功率比及功率放大器的線性化等技術便成為現今研究的焦點，功率放大器的線性化技術及降低訊號峰均功率比技術均可為功率放大器的功率使用效能提供良好的改進，同時也吸引了許多研究分別在這二方面進行，但同時將這二項技術合併使用和探討並不多見，在本篇論文中將會將這二項技術合併使用並對其效能及結果進行探討及分析，其中在降低訊號峰均功率比技術上採用metric based amplitude predistortion (MBAP) 或 phase reversal (PR)技術，而在功率放大器線性化方面則是使用memory polynomial predistorter (MPPD) 配合非直接學習架構 (indirect learning, IDL) 或是直接學習架構 (direct learning, DL)以及使用least square (LS) 及Least Mean Square(LMS)演算法，而在放大器方面則是分別使用Wiener 及Wiener-Hammerstein放大器模型，最後並將以total degradation (TD)來做系統的效能評估，最終模擬結果顯示，本篇論文中所提出的結合降低峰均功率比技術與多項式預扭器技術可以有效地改善系統的功率效能，且特別是當PR技術結合IDL預扭技術使用LMS演算法時可獲得最佳的效能。

Since the nonlinear effect caused by the High Power Amplifier (HPA), wireless communication system suffers from the in-band distortion and out-band emission. Orthogonal frequency division multiplexing (OFDM) scheme with high spectral efficiency and resistance to multi-path distortion has become a key modulation format in wireless communications. Its inherent high peak to average power ratio (PAPR) severely limits the power efficiency of the operating power amplifier. In addition to the PAPR problem, the power amplifier with nonlinear characteristic also restricts the power efficiency. In order to alleviate these drawbacks, PAPR reduction and PA linearization have both been extensively studied to deal with this important issue.
In this thesis, we propose a new approach which combines the metric based amplitude predistortion (MBAP) / phase reversal (PR) PAPR reduction scheme and memory polynomial predistorter (MPPD) with indirect learning (IDL) / direct learning (DL) architecture. A comparative study of performance of different combinations using Wiener power amplifier model / Wiener-Hammerstein power amplifier model is presented in terms of total degradation (TD). Simulation results indicate that our proposed combination approach can effectively improve the performance. In particular, MPPD using LMS algorithm with PR is the best one to the power efficiency

中文摘要 i
ABSTRACT ii
CONTENTS iii
LIST OF FIGURES vi
LIST OF TABLES ix
Chapter 1 簡介 1
1.1 歷史與背景 1
1.1.1 放大器線性化技術 1
1.1.2 降低峰均功率比(PAPR)技術 3
1.2 正交分頻多工技術 7
Chapter 2 功率放大器及預扭器模型 12
2.1 功率放大器模型 12
2.1.1 無記憶性的功率放大器模型 14
2.1.2 無記憶性多項式模型 15
2.1.3 具記憶性的功率放大器模型 18
2.1.4 Volterra 級數 18
2.1.5 多項式模型 19
2.1.6 Wiener模型 20
2.1.7 Wiener-Hammerstein 模型 20
2.2 預扭器模型及架構 21
2.2.1 在間接學習架構下的記憶性多項式預扭器(MPPD) 21
2.2.2 在直接學習架構下的記憶性多項式預扭器(MPPD) 23
2.3 演算法 24
2.3.1 在間接學習架構中搭配LS演算法 24
2.3.2 在間接學習架構中搭配LMS演算法 25
2.3.3 在直接學習架構中搭配LS演算法 26
2.3.4 在直接學習架構中搭配LMS演算法 27
Chapter 3 降低PAPR之振幅預扭及相位轉向技術 29
3.1 振幅預扭技術 29
3.2 相位轉向技術 35
Chapter 4 結合降低峰均功率比(PAPR)技術與多項式預扭器(MPPD)技術之OFDM系統模型 38
4.1 使用間接學習(IDL)架構之系統模型 38
4.2 使用直接學習(DL)架構之系統模型 40
Chapter 5 模擬與討論 42
5.1 系統效能之量測 42
5.2 使用降低PAPR技術之效能分析 44
5.2.1 降低PAPR技術之MBAP/PR技術效能分析 44
5.2.2 使用Wiener PA model 46
5.2.3 使用Wiener-Hammerstein PA model 48
5.3 使用MPPD 技術之效能分析 50
5.3.1 使用Wiener PA model 配合使用LS_IDL、LMS_IDL和LMS_DL技術 52
5.3.2 使用Wiener-Hammerstein PA model配合使用LS_IDL、LMS_IDL和LMS_DL技術 53
5.4 使用降低PAPR技術合併MPPD技術之效能分析 55
5.4.1 使用Wiener PA model 55
5.4.2 使用Wiener-Hammerstein PA model 60
5.5 總結 63
Chapter 6 結論 66

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