正交分頻多工系統具有高的頻寬效率，且能夠對抗多重路徑衰減干擾的優點；其主要的缺點為具有高的峰值對平均功率比 (high peak to average power ratio, PAPR)。由於高的峰對平均功率比，使得經過高功率放大器的信號，受到放大器操作點的影響而被截掉，引起失真。最簡單消除的方法，是降低傳送到放大器信號的強度，使得放大器的操作點落在線性區；但此作法會造成功率效益過低，不是合理解決方法。基頻的數位預扭器 (digital predistorter, DPD)能夠有效線性化放大器以提升功率效益。當數信號經過非線性放大器時，其輸出會有頻譜再生的現象，而造成鄰近通道的干擾。因此，線性化功率放大器降低位元錯誤率的同時，也必須能夠抑制頻譜再生。
在本篇論文中，我們將分析在正交分頻多工與正交振幅調變系統下，使用DPD來補償由高功率放大器所引起的非線性失真，使之線性化。我們將使用到多項式 (Polynomial)預扭器補償有記憶性的功率放大器 (power amplifier, PA)，而在PA方面使用的是Wiener PA 以及Wiener-Hammerstein PA。在補償的架構上，在補償的預扭器上，我們考慮兩種情形，一個是記憶長度為0 (M = 0)、階數 (order)長度為5 (K = 5)的無記憶性預扭器，一個是記憶長度為2、秩序長度為5有記憶性預扭器。我們使用間接學習架構 (Indirect Learning Architecture, ILA)與直接學習架構 (Indirect Learning Architecture, DLA)結合多項式模組，藉由Least square (LS)、Least Mean Square (LMS)和Normalized Least Mean Square (NLMS)求得DPD的多項式係數，達到線性化的功率放大器。在分析方面，我們將藉由TD (Total Degradation)和PSD (Power Spectral Density)來分析系統效能來比較不同的系統模型。

The orthogonal frequency division multiplex (OFDM) system has an advantage of high bandwidth efficiency, and is able to resist the multipath fading interference; its major disadvantage is high peak to average power ratio (PAPR). The signal passing a power amplifier can be clipped depending on the operation point of the power amplifier. This clipped signal results in distortion. The easiest way to counteract the distortion is to reduce the signal power which enters the power amplifier. This makes the operation point of the power amplifier drop to the linear region which will significantly reduce power efficiency. Digital baseband predistorter (DPD) can efficiently linearize the power amplifier so as to improve the power efficiency. In addition, it can reduce the bit error rate and also suppress spectrum regrowth.
In this thesis, we analyze the use of DPD to compensate the nonlinear distortion caused by the power amplifier in OFDM systems. We use polynomial predistorters to compensate the memory power amplifier. In the PA model, we use the Wiener PA and Wiener-Hammerstein PA. In the predistorter model, we use memoryless polynomial predictorter and memory polynomial predictorter. We employ indirect learning architecture and direct learning architecture combined with polynomial predistorters, whose coefficients are adapted using least-squares (LS), least-mean-square (LMS) and normalized least-mean-square (NLMS) algorithms. Finally, the total degradation (TD) and power spectrum density (PSD) are used as performance measures to compare different system models.

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