近年來通訊技術不斷的提升，人們對於資料的傳輸速度及資料吞吐量的需求也隨之增加。因此常見的方法就是增加基地台與手機的天線數目，通常基地台設有數十至數百根天線來增加資料的吞吐量，但因為功率放大器的數量龐大，在硬體實現上必需考量成本，較低成本的功率放大器線性度不佳，無法處理高功率峰均值比 (Peak-to-Average-Power-Ratio, PAPR) 的系統。參考文獻中所提到的低功率峰均值預編碼可以解決這個問題，但因其使用的梯度下降演算法計算複雜度極高，大幅降低其求解的速度，本篇論文提出期望傳播演算法(Expectation Propagation, EP) 來求解，雖然比近似信息演算法 (Approximated Message Passing, AMP) 的複雜度高，但是能夠改良近似訊息演算法在常數封包預編碼效能較差的問題，而且相較於序列式梯度下降演算法，雖然有較高的運算複雜度，但能有超越序列式梯度下降演算法的效能，而使得期望傳播演算法相較於序列式梯度下降演算法在大規模多天線預編碼系統上有更好的效能表現。

With growing applications in multimedia, we have increasing demand in the higher data transmission rate and the more effective data throughput. One way to meet this demand is to use the massive antenna technique where one base station is equipped with tens to hundreds antennas to increase data throughput. However, we have to consider the tradeoff between the cost and the effectiveness of power amplifiers. Low cost power amplifier has poor linearity and cannot deal with high peak-to-average-power-ratio (PAPR) systems. The low PAPR precoding such as the gradient descent algorithm (GD) and sequential gradient descent algorithm (SGD) were proposed to solve this problem, they have highly computational complexity and thus significantly reduce the speed of system. In this thesis, we use the expectation propagation (EP) algorithm to solve this problem. Although the EP algorithm is much more complicated than the approximated message passing (AMP) algorithm, it can improve the poor performance of the AMP algorithm as the constant envelope precoding is considered. Compared with the SGD algorithm, the EP algorithm has the higher computation complexity while with performance better than the SGD, making it a preference technique in the massive MIMO precoding system.

論文審定書 i
誌謝 ii
摘要 iii
Abstract iv
目錄 v
圖次 vi
表次 viii
第一章 緒論 1
1.1 前言及研究動機 1
1.2 論文架構 4
第二章 系統架構 5
2.1 多輸入多輸出系統架構 5
2.2 多天線常數封包預編碼系統 7
第三章 演算法 9
3.1 梯度下降法 (Gradient Descent) 9
3.2 序列式梯度下降法 (Sequentional Gradient Descent) 11
3.3 近似訊息演算法 (Approximate Message Passing) 14
3.4 期望傳播演算法 (Expectation Propagation) 24
第四章 模擬與結果分析 31
4.1 收斂情況分析 31
4.2 大規模多天線常數封包預編碼系統 39
4.3 小規模多天線常數封包預編碼系統 42
4.4 大規模多天線環形預編碼系統 45
第五章 結論 47
參考文獻 48

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