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平均功率比 (peak to average power ratio, PAPR)，由於高的峰均值功率比，使得經過功率放大器(power amplifier，PA)的訊號，受到放大器操作點的影響而被截掉，引起失真。然而，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，此外，當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。為了能夠有效的解決這些問題，降低峰均功率比以及功率放大器的線性化等技術便成為現今研究的課題。在本篇論文中，以相位轉向技術(phase reversal, PR)作基礎，延伸出三種改進的方法，其一是延展相位轉向技術(Extended PR)，其二是反覆相位轉向技術(Iterative PR)，其三把這兩項優點合併起來為結合延展及反覆相位轉向技術(Combined PR)，接著，我們將在有無旁資訊的情況下，提出將這四種技術結合Hadamard 轉換和Companding技術，可以改善PAPR效能，會有加成的效果，壓縮因子 愈大PAPR改善愈多，相對的錯誤率增加，因此，在系統錯誤率及降低PAPR需要找到一個最佳的平衡點。另外，在功率放大器線性化方面則是使用memory polynomial predistorter (MPPD) 搭配間接學習架構 (indirect learning, IDL) 或是直接學習架構 (direct learning, DL) 利用 least square (LS) 及least mean square (LMS)演算法求出預扭器參數，放大器方面則是使用Wiener 及Wiener-Hammerstein兩個放大器模型，最後將把相位轉向技術與多項式預扭器技術合併使用並以total degradation (TD)和功率頻譜密度(power spectral density, PSD)來做系統的效能評估，還有錯誤率的曲線圖，可以有效地改善整體的系統效能，且特別是當Iterative PR技術結合IDL預扭器技術使用LMS演算法時可獲得最佳的效能表現。

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 (PA) can be clipped depending on the operation point of the PA. This clipped signal results in distortion. Digital 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 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 uses Hadamard /Companding transform to combine the metric based phase reversal (PR)/iterative PR/extended PR/combined PR PAPR reduction schemes. And we improve the performance of this issue with side information or without side information in the situation. Then, we use the PAPR reduction schemes combination of 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) and power spectral density (PSD). Simulation results indicate that improvement of PAPR efficiency by adding hadamard transform and companding technique and our proposed combination approach can effectively improve the performance. In particular, MPPD using LMS algorithm with iterative PR is the best one to the power efficiency.

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