本論文使用第六代行動通訊系統所規劃的毫米波頻段，進行室內的通訊品質研究，在高頻的情況下使用同步訊息與電力傳輸技術，同時進行無線充電及資訊，而為了降低毫米波帶來的路徑衰減，本論文同時採用波束成型的技術，並在同一環境同時擺設只需進行無線充電的無線節點，在同時考慮SWIPT節點以及WPT節點情況下，利用演算法結合陣列天線的波束成型技術，進行室內通訊品質及充電效率的優化。
計算位元錯誤率與能量採集效率來分別評估通訊品質與充電效率，並利用自我適應之動態差異型演化法設定錯誤率、SWIPT節點和WPT節點的基本要求限制，使其能同時考慮最高錯誤率及最小充電效率，在將兩種節點擺在任意均勻之室內空間位置上，分別對SWIPT節點相較WPT與傳送天線端距離較近、較遠及相近的三種情況進行能量分流優化，結果顯示，透過演算法與多目標函數的優化下，兩種節點皆能達到限制的基本的通訊品質及最低的能量採集效率，雖然在SWIPT距離Tx相較WPT距離Tx較遠與相近的情況下，不管是總距離較遠及總距離較近，結果都只有些微提升，但在SWIPT距離Tx相較WPT距離Tx較近，且總距離較近的情況下能提升86.7%的充電效率，總距離較遠也能有7.87%的提升，系統效能整體來說皆有所提升，且此錯誤率皆能在所有的情況下滿足條件。

This paper uses the millimeter wave frequency band planned by the sixth-generation mobile communication system to conduct indoor communication quality research. It uses synchronous information and power transmission technology under high frequency conditions to simultaneously performs wireless charging and information transmission. In order to reduce the millimeter wave band attenuation of the channel path, this paper also uses beamforming technology, and deploys wireless nodes that only need wireless charging in the same environment at the same time. Considering both SWIPT nodes and WPT nodes, the algorithm is combined with the beamforming technology of array antennas to optimize the indoor communication quality and charging efficiency.
This paper performs bit error rate and energy collection efficiency calculation to evaluate communication quality and charging efficiency respectively, and uses self-adaptive dynamic differential evolution method to set the error rate, SWIPT node and WPT node's basic requirements limit, so that it can consider the highest error rate and the minimum charging efficiency at the same time. The optimization for the three situations which the distance the  between the transmitting antenna and individual SWIPT and the WPT node is closer、farther、similar when the two nodes are placed at any uniform indoor clear position. The results show that through the optimization of algorithms and multi-objective functions, both nodes can achieve the limited basic communication quality and the lowest energy collection efficiency. However, in order to further optimize the results, energy distribution mechanism in SWIPT node is added to the optimization and adjustment of the algorithm.  Although there was only a slight improvement in both total and total distances when SWIPT was closer to TX than WPT was closer to TX, there was an 86.7% improvement in charging efficiency when SWIPT was closer to TX than WPT was closer to TX, and a 7.87% improvement when the total distance was longer. The overall system performance is improved, and the error rate can meet the conditions in all cases.

第一章	緒論	1
1.1 	研究背景	1
1.1.1 毫米波技術的優勢	2
1.1.2 波束成型	4
1.1.3 同步訊息與電力傳輸(SWIPT)	5
1.1.4 巨量多輸入多輸出	5
1.1.5 演算法	5
1.2 研究動機	6
1.3研究貢獻	7
第二章	系統介紹	8
2.1 	同步訊息與電力傳輸	8
2.1.1 射線彈跳追蹤法	12
2.1.2 利用射線追蹤法計算出頻域響應	16
2.1.3 利用何米特法與快速反傅立葉轉換計算出時域響應	18
2.2 通道容量	20
2.3 位元錯誤率的計算	20
2.4 波束成型技術	23
第三章	演算法介紹及分析	27
3.1 自適應動態差分演算法	27
第四章	數值模擬結果	36
4.1   模擬環境與建構	36
4.2   SADDE演算法和其他參數相關設定	38
4.3 發射器距離SWIPT的節點較WPT的節點遠	40
4.3.1  演算法優化之迭代圖	40
4.3.2  能量採集優化之迭代圖	41
4.3.3  場型和環境	42
4.3.4  系統效能整理	46
4.4 發射器距離SWIPT的節點較WPT的節點近	46
4.4.1  演算法優化之迭代圖	46
4.4.2  能量採集優化之迭代圖	47
4.4.3  場型和環境	48
4.4.4  系統效能整理	52
4.5發射器距離SWIPT的節點和WPT的節點相近	52
4.5.1  演算法優化之迭代圖	52
4.5.2  能量採集優化之迭代圖	54
4.5.3  場型和環境	55
4.5.4  系統效能整理	58
第五章 能量分流的優化調整	58
5.1發射器距離SWIPT的節點較WPT的節點遠	59
5.1.1  演算法優化之迭代圖	59
5.1.2  能量採集優化之迭代圖	61
5.1.3  場型和環境	64
5.2發射器距離SWIPT的節點較WPT的節點近	68
5.2.1  演算法優化之迭代圖	68
5.2.2  能量採集優化之迭代圖	69
5.2.3  場型和環境	72
5.3發射器距離SWIPT的節點和WPT的節點相近	77
5.3.1  演算法優化之迭代圖	77
5.3.2  能量採集優化之迭代圖	78
5.3.3  場型和環境	81
第六章 結論	86
參考文獻		88

圖目錄
圖2. 1無線功率傳輸示意圖	9
圖2. 2 無線功率傳輸接收器架構示意圖	10
圖2. 3 無線功率傳輸技術之射頻能量採集架構示意圖	11
圖2. 4  SBR/Image 程式流程圖	15
圖2. 5 信號經過何米特程序與快速反傅立葉轉換處理後之結果	19
圖2. 6 何米特程序的信號處理步驟與快速反傅立葉轉換過程	19
圖2. 7 波束成型技術應用的示意圖	23
圖2. 8 座標系統示意圖	24
圖2. 9 環形陣列天線示意圖	26
圖3. 1 粒子群聚法流程圖	28
圖3. 2 自我適應之動態差異型演化法流程圖	29
圖3. 3 自我適應之動態差異型進化法中突變方法一的示意圖	31
圖3. 4 自我適應之動態差異型進化法中突變方法二的示意圖	32
圖3. 5 自我適應之動態差異型進化法中的交配向量於一個二維目標函數 等位線圖描述的示意圖	34
圖4. 1 室內環境示意圖	37
圖4. 2 圓形天線陣列的擺設方式	38
圖4.3 兩組距離發射器較遠情況之迭代過程	41
圖4.4 兩系統之能量採集迭代過程	42
圖4.5 Rx1和Rx2之環境簡圖	43
圖4. 6 場型圖	44
圖4. 7  Rx1和Rx2之環境簡圖	45
圖4. 8場型圖	45
圖4. 9  兩組距離發射器Tx較近系統的迭代過程	47
圖4. 10 兩系統之能量採集迭代過程	48
圖4. 11  Rx1和Rx2之環境簡圖	49
圖4. 12 場型圖	50
圖4. 13  Rx1和Rx2之環境簡圖	51
圖4. 14 場型圖	51
圖4. 15 兩組距離發射器Tx一樣系統的迭代過程	53
圖4. 16  200代-500代的變化圖	53
圖4. 17 兩系統之能量採集迭代過程	54
圖4. 18  Rx1和Rx2之環境簡圖	55
圖4. 19 場型圖	56
圖4. 20  Rx1和Rx2之環境簡圖	57
圖4. 21 場型圖	57
圖5. 1 有無α優化之迭代過程	60
圖5. 2  400-500代變化	60
圖5. 3  SWIPT節點在能量採集上有無α優化的比較	62
圖5. 4  WPT節點在能量採集上有無α優化的比較	62
圖5. 5  Rx1(9,7)Rx2(2,6)簡易環境圖	64
圖5. 6  α固定0.5之場型	65
圖5. 7  α可調之場型	65
圖5. 8  RX1(7,2)RX2(3,6) 簡易環境圖	66
圖5. 9  無α之場型	67
圖5. 10 有α之場型	67
圖5. 11 有無α優化之迭代過程	68
圖5. 12  400-500代變化	69
圖5. 13  SWIPT節點在能量採集上有無α的比較	70
圖5. 14  WPT節點在能量採集上有無α的比較	71
圖5. 15  RX1(5,8), RX2(2,2)簡易環境圖	73
圖5. 16 無α之場型	73
圖5. 17 有α之場型	74
圖5. 18  RX1(6,6), RX2(2,5)簡易環境圖	75
圖5. 19 無α之場型	75
圖5. 20 有α之場型	76
圖5. 21 有無α優化之迭代過程	77
圖5. 22  200-500 代之迭代過程	78
圖5. 23  SWIPT節點在能量採集上有無α的比較	79
圖5. 24  WPT節點在能量採集上有無α的比較	80
圖5. 25  RX1(9,8), RX2(3,1) 簡易環境	82
圖5. 26 無α之場型	82
圖5. 27 有α之場型	83
圖5. 28  RX1(7,8), RX2(2,7) 簡易環境圖	84
圖5. 29 無α之場型	84
圖5. 30 有α之場型	85

表目錄
表4. 1 效能統整	46
表4. 2 效能統整	52
表4. 3 效能統整	58
表5. 1第一組RX1(9,7),RX2(2,6)	63
表5. 2 第二組RX1(7,2),RX2(3,6)	63
表5. 3 第一組RX1(5,8),RX2(2,2)	71
表5. 4 第二組RX1(6,6),RX2(2,5)	72
表5. 5 第一組RX1(9,8),RX2(3,1)	80
表5. 6 第二組RX1(7,8),RX2(2,7)	81

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