本論文之研究目的以多輸入多輸出和中繼器來提升通訊品質之研究。利用射線彈跳追蹤法(Shooting and Bouncing Ray/Image Techniques, SBR/Image Techniques)，求得超寬頻（Ultra Wideband, UWB）通訊與無線區域網路（Wireless Local Area Network, WLAN）系統的通道特性參數。第一部份研究求得多輸入多輸出(Multiple-Input Multiple-Output, MIMO) WLAN系統在六種不同走道的頻率響應和脈衝響應，並去計算和比較MIMO-WLAN系統的通道特性。這六種不同的走道分別為：1.矩形截面直線走道、2.矩形截面圓弧走道、3.拱門截面直線走道、4.拱門截面圓弧走道、5.矩形截面L形走道、6.矩形截面T形走道。若SNR定義為接收機前端之信號平均功率對雜訊功率的比值，從模擬結果得到T形走道的通道容量最大，且矩形截面走道的通道容量普遍大於弧形截面的走道。
第二部份以基因演算法（Genetic Algorithms, GA）、粒子群聚最佳化法（Particle Swarm Optimization, PSO）、非同步粒子群聚最佳化法（Asynchronous Particle Swarm Optimization, APSO）與動態差異型演化法（Dynamic Differential Evolution, DDE）來最佳化室內MIMO-WLAN通訊系統之發射天線位置。計算出發射天線與接收天線間之通道頻率響應，並求出通訊過程中的通道容量。將演算法和射線彈跳追蹤法結合模擬複雜環境。藉由模擬去計算MIMO-WLAN系統在真實環境下之通道容量。以演算法找到最佳發射天線位置，使系統的通道容量提升。選用適當發射天線的位置預測無線電波傳輸時的特性，可以提升通訊品質。
第三部份研究的目標是希望透過找到最佳發射與中繼天線位置，使系統的錯誤率降低。利用GA、PSO、APSO與DDE演算法最佳化室內的發射與中繼天線位置，探討在UWB系統通訊下對位元錯誤率、失效率的影響。數值結果顯示，此研究結果能幫助通訊品質改善，使其每個接收點位元錯誤率能達到標準。
第四部份研究同頻干擾(Co-Channel Interference, CCI)對MIMO-WLAN系統通道容量的影響。首先，在MIMO-WLAN系統中，計算出有無同頻干擾情況下其通道容量，其中，干擾源包括單一干擾和多根干擾。其次，使用均勻線性陣列（Uniform Linear Array, ULA）天線和極化分集陣列（Polarization Diversity Array, PDA）天線，來探討對於系統通道容量的影響。在MIMO-WLAN中傳送端、接收端和多個同頻干擾皆採用此兩種天線陣列探討。研究結果顯示，沒有同頻干擾的情況下，均勻線性陣列天線相較於極化分集陣列天線的通道容量高。有同頻干擾時，極化分集陣列天線的通道容量比均勻線性陣列天線高。

Quality improvement of communication systems by multiple-input multiple-output and relays in real environments are investigated. The channel statistics parameters of ultra wide band (UWB) and wireless local area network (WLAN) are computed by applying shooting and bouncing ray/image (SBR/Image) techniques. First, a comparison of multiple-input multiple-output (MIMO) WLAN communication characteristics for six different geometrical shapes is investigated. These six shapes include the straight shape corridor with rectangular cross section, the straight shape corridor with arched cross section, the curved shape corridor with rectangular cross section, the curved shape corridor with arched cross section, L-shape corridor, and T-shape corridor. The frequency responses and impulse responses of these corridors are computed by applying SBR/Image techniques. By using the frequency responses and impulse response of these multi-path channels, the channel capacity and statistic parameters for these six corridors could be obtained. 
The second part, the genetic algorithm （GA）, particle swarm optimization （PSO）, asynchronous particle swarm optimization （APSO） and dynamic differential evolution （DDE） are used to optimizing the objective functions (criterion for measuring the effectiveness of the obtained optimized algorithm solution) and solved in indoor MIMO-WLAN  communication system. The optimal locations of the transmitter antenna for channel capacity in indoor environment MIMO-WLAN wireless communication systems are evaluated in the whole indoor environment. The channel capacity is chosen as the objective function. Based on the SBR/Image performance, the channel capacity for any given location of the transmitter can be computed. The optimal transmitting antenna location for maximizing the channel capacity is searched by algorithms. Obtained simulation results illustrate the feasibility of using the integrated ray-tracing, and optimization methods to find the optimal transmitter locations in determining the optimized channel capacity of a wireless network. Numerical results show that the performance for increasing of channel capacity by optimization algorithm is quite good. The investigated results can help communication engineers improve their planning and design of indoor wireless communication.
The third part, an optimization procedure for the location of the transmitter antenna and relay transceiver in UWB wireless communication system is presented. The impulse responses of different transmitter antenna and transceiver locations are computed by SBR/Image techniques and inverse fast Fourier transform (IFFT). By using the impulse responses of these multi-path channels, the bit error rate (BER) performance for binary pulse amplitude modulation (BPAM) impulse radio UWB communication system are calculated. Based on the BER performance, the outage probability for any given transmitter antenna and relay location of the transceiver can be computed. The optimal transmitter antenna and relay antenna location for minimizing the outage probability is searched by GA, PSO, APSO and DDE. Numerical results have shown that our proposed method is effective to find the optimal location for transmitter antenna and relay antenna.
The fourth part, the dissertation focuses on the research of channel capacity of MIMO-WLAN system with co-channel interference (CCI) in indoor environment. The channel capacities are calculated based on the realistic environment. First, channel capacities of the MIMO-WLAN system without and with CCI are calculated for both single and multiple transmitting antennas of the CCI. Next, the channel capacities by using simple uniform linear array (ULA) and polarization diversity array (PDA) deployment are calculated. According to the results, for the case without CCI channel capacity of ULA is better than that of PDA in indoor wireless communication. However, the channel capacity for the PDA is better than that for of ULA when interference exists.

目錄
中文摘要…………………………………………………………………I
英文摘要………………………………………………………………III
第一章 簡介	1
1.1 研究計畫之背景	1
1.2 研究動機	13
1.3 本研究之貢獻	16
1.4 國內外有關本計畫之研究情況	18
1.5 各章內容簡述	19
第二章 傳輸通道系統描述	20
2.1	無線電波傳播通道分析	20
2.2  通道計算模型分析	21
2.2.1 利用射線追蹤法計算出頻域響應	22
2.2.2 利用何米特法與快速反傅立葉轉換計算出時域響應	25
2.3 射線彈跳追蹤法程式流程分析	28
2.4 二位元脈衝振幅調變位元錯誤率系統架構	32
2.4.1 發射訊號波形	32
2.4.2 位元錯誤率之計算	33
第三章 多輸入多輸出系統理論	40
3.1 多輸入多輸出窄頻系統表示	40
3.2 多輸入多輸出窄頻系統通道容量	43
3.2.1 建立在 CSI–B 狀態下	44
3.2.2 建立在只有 CSI–R 狀態下	45
3.2.3 中斷容量Outage Capacity和統計容量Ergodic Capacity 46
3.3 影響因素MIMO容量	47
3.3.1 空間自由度Spatial Degree of Freedom	 47
3.3.2 特徵矩陣Eigenmatrix和條件數目Condition number	48
3.3.3 空間關係Spatial Correlation	49
3.4 干擾源之多輸入多輸出窄頻系統的通道容量	49
3.4.1 系統表示	50
3.4.2 建立在CSI-B的通道容量表示	54
3.5 MIMO–WLAN系統之通道容量	55
3.6 通道正規化（Channel Normalization）	56
第四章 基因演算法、動態差異型演化法與非同步粒子群聚法 58
4.1 基因演算法（Genetic Algorithms）	58
4.1.1基因演算法基本概念	58
4.1.2 基因演算法中的運算方式	61
4.2 差異型演化法（Differential Evolution）	67
4.3 動態差異型演化法（Dynamic Differential Evolution）76
4.4 粒子群聚最佳化法（Particle Swarm Optimization）	79
4.5 非同步粒子群聚最佳化法（Asynchronous Particle Swarm Optimization）	86
第五章 在MIMO–WLAN系統下比較各種走道的通道特性	90
5.1 摘要	90
5.2 數值模擬結果	90
5.2.1 模擬環境與參數設定	90
5.2.2 模擬結果分析與比較	97
5.3 結論	105
第六章 調整MIMO–WLAN發射天線位置達到通道容量提升	107
6.1 摘要	107
6.2 數值模擬結果	108
6.2.1 模擬環境與參數設定	108
6.2.2 模擬結果分析與比較	110
6.3 結論	117
第七章 最佳化室內發射和中繼天線位置達到位元錯誤率降低 119
7.1 摘要	119
7.2 數值模擬結果	120
7.2.1 模擬環境與參數設定	120
7.2.2 最佳化發射天線位置	124
7.2.3 最佳化發射和中繼天線位置	131
7.2.4 模擬結果分析與比較	135
7.3 結論	139
第八章 同頻干擾對MIMO–WLAN系統其通道容量分析	141
8.1 摘要	141
8.2 數值模擬結果	141
8.2.1 模擬環境與參數設定	141
8.2.2 模擬結果分析與比較	145
8.3 結論	149
第九章 結論	151
參考文獻	155
Publication of S. H. Liao	174
 
圖目錄
圖2.1 求得通道脈衝響應的步驟	22
圖2.2 何米特程序的信號處理步驟與快速反傅立葉轉換過程 27
圖2.3 SBR/Image 程式流程圖	31
圖2.4 二位元脈衝振幅調變位元錯誤率系統架構圖	32
圖2.5 傳送高斯二次微分脈波的波型	33
圖2.6 FCC對室內及室外超寬頻系統的頻段及輻射能量限制	34
圖2.7 通訊傳輸模型	35
圖3.1 多輸入多輸出窄頻系統矩陣示意圖	40
圖3.2 多輸入多輸出窄頻系統示意圖	42
圖3.3 建立在 CSI–B 狀態下多輸入多輸出窄頻系統的等效架構圖	43
圖3.4 干擾源的多輸入多輸出窄頻系統矩陣示意圖	51
圖3.5 干擾源的多輸入多輸出窄頻系統示意圖	52
圖3.6 建立在CSI-B狀態下之干擾源的多輸入多輸出窄頻系統的等效架構圖	53
圖4.1 基因演算法流程圖	61
圖4.2 單點交配示意圖	64
圖4.3 差異型演化法流程圖	68
圖4.4 差異型進化法中突變方法一的示意圖	71
圖4.5 差異型進化法中突變方法二的示意圖	72
圖4.6 差異型進化法中突變方法三的示意圖	72
圖4.7 差異型進化法中交配向量結構示意圖	74
圖4.8 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	75
圖4.9 動態差異型型演化策略法流程圖	78
圖4.10 粒子群聚法流程圖	81
圖4.11 粒子群聚法中於二維目標函數等位線圖	82
圖4.12 二維問題中，三種不同邊界條件示意圖。xkid、與vkid、表示更新後的粒子位置與速度。	85
圖4.13 非同步粒子群聚法流程圖	89
圖5.1（a）矩形截面直形走道之立體圖	92
圖5.1（b）拱形截面直形走道之立體圖	92
圖5.1（c）矩形截面彎曲形走道之立體圖	93
圖5.1（d）拱形截面彎曲形走道之立體圖	93
圖5.1（e）矩形截面L形走道之立體圖	94
圖5.1（f）矩形截面T形走道之立體圖	94
圖5.2（a）直形走道之頂視圖	95
圖5.2（b）彎曲走道之頂視圖	95
圖5.2（c）L形走道之頂視圖	96
圖5.2（d）T形走道之頂視圖	96
圖5.3 六種走道其通道容量比較圖（SNR定義為發射端之信號平均功率對雜訊功率的比值）	101
圖5.4 六種走道其通道容量比較圖（SNR定義為接收機前端之信號平均功率對雜訊功率的比值）	104
圖6.1 模擬環境平面圖，長9.2公尺、寬為10公尺、高度為3公尺。TxCenter為發射天線放置於環境中央。	110
圖6.2 接收點均勻分佈在桌面其通道容量比較圖	111
圖6.3 接收點均勻分佈在室內環境其代數對通道容量在搜尋過程變化趨勢圖	112
圖6.4 接收點均勻分佈在室內環境其通道容量比較圖	114
圖6.5 接收點均勻分佈在室內環境其代數對通道容量在搜尋過程變化趨勢圖	115
圖7.1 辦公室平面圖其長為20 m、寬為20 m、高度為3 m。TxCenter為發射天線放置於環境中央。	122
圖7.2 辦公室放入100個接收點之分佈圖	123
圖7.3 僅一個發射天線時其發射天線位置。（◎）傳送點位於室內環境中心點TxCenter。（▽）GA最佳傳送位置TxGA。（△）PSO最佳傳送位置TxPSO。（ⓧ）APSO最佳傳送位置TxAPSO。（□）DDE最佳傳送位置 TxDDE。	125
圖7.4 室內環境中心點TxCenter 其失效區域（位元錯誤率大於10–6）	127
圖7.5 最佳發射位置TxGA其失效區域（位元錯誤率大於10–6）	128
圖7.6 最佳發射位置TxPSO其失效區域（位元錯誤率大於10–6）	129
圖7.7 最佳發射位置TxAPSO其失效區域（位元錯誤率大於10–6）	130
圖7.8 最佳發射位置TxDDE其失效區域（位元錯誤率大於10–6）	131
圖7.9 各一個發射和中繼天線位置Tx53（5m, 9m, 0.8m）和Relay58（15m, 9m, 0.8m）其失效區域（位元錯誤率大於10–6）	133
圖7.10 最佳發射和中繼天線位置其失效區域（位元錯誤率大於10–6）。（▽）和（▼）為GA最佳發射和中繼天線位置TxGA和RelayGA。（△）和（▲）為PSO最佳發射和中繼天線位置TxPSO和RelayPSO。（ⓧ）和（●）為APSO最佳發射和中繼天線位置TxAPSO和RelayAPSO。（□）和（■）為DDE最佳發射和中繼天線位置 TxDDE和RelayDDE。	134
圖7.11 一個發射天線時下比較四種演算法其代數對失效率在搜尋過程變化趨勢圖	137
圖7.12 各一個發射和中繼天線下比較四種演算法其代數對失效率在搜尋過程變化趨勢圖	138
圖8.1 模擬環境平面圖，長4公尺、寬為5.2公尺、高度為2.5公尺。	143
圖8.2 (a) 均勻線性陣列示意圖	144
圖8.2 (b) 極化分集陣列示意圖	144
圖8.3 3×3–ULA和3×3–PDA在有無同頻干擾情況下其平均通道容量	146
圖8.4 3×3–ULA在有CCI–ULA、CCI–PDA和沒有加入同頻干擾的平均通道容量	148
圖8.5 3×3–PDA在有CCI–PDA、CCI–ULA和沒有加入同頻干擾的平均通道容量	149



 
表目錄
表3.1 對應不同系統中空間自由度的數目	47
表4.1 基因演算法相關名詞解釋與中英對照表	59
表5.1 混凝土的材質係數	91
表5.2 六種不同幾何結構走道在MIMO–WLAN通訊下的通道參數一欄表	99
表5.3 最大反射次數10次與50次其六種不同幾何結構走道在MIMO–WLAN通訊下的通道參數一欄表	100
表6.1 在MIMO–WLAN通訊下四種演算法的通道參數一覽表（τMED 和τRMS）	116
表7.1 在UWB通訊下其失效點個數一覽表	136
表7.2 在UWB通訊下四種演算法的通道參數一覽表（τMED和τRMS）	136

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