臺灣博碩士論文加值系統

多輸入多輸出的天線陣列系統，可增加傳輸率及通訊可靠度，但相對的硬體成本及運算複雜度也提高許多。本論文考量在空時區塊編碼-空間多工混合系統中，總功率為定值情況下，使通道容量為最佳化的天線選擇與功率分配，一方面可降低硬體的成本，一方面可提升傳輸率及通訊可靠度。我們提出一種互助式的演算法，利用優序基因演算法找出最佳選擇天線組，並提供此資訊給實數基因演算法或粒子群優化演算法求出最佳功率分配組。
經由電腦模擬結果比較，顯示本論文所提出的方法與混合型的優序基因與實數基因演算法有近似相同的最佳通道容量，但所提的運算複雜度比較低；此外，比其它現今存在的方法，例如最佳天線選擇(等功率分配)及混合型的優序基因與二元基因演算法，有較佳的通道容量，與較低的運算複雜度。

ABSTRACT

MIMO antenna systems can increase data transmission rate and reliability, but also require larger hardware cost and computational complexity. To reduce hardware cost while maintaining the merits of data rate and reliability, this thesis considers a hybrid system with space-time encoding and spatial multiplexing, under which the joint antenna selection and power allocation problem is solved by maximizing the channel capacity subjected to a fixed total transmission power. To achieve this goal, we propose a cooperative method that uses the priority genetic
algorithm (PGA) to determine the best set of selected antennas, passes this information to the real-parameter genetic algorithm (RGA) or particle swarm optimization (PSO) to find the best power allocation set.
Simulation results reveal that the proposed method has nearly the same optimal channel capacity as that of the hybrid genetic algorithm (combining PGA and RGA), but requires less computational complexity. In addition, the proposed method has larger channel capacity and requires less computational complexity than the other existing methods, such as the optimal antenna selection with equal power allocation and the hybrid genetic algorithm (combining PGA and BGA).

第一章 導論…………………………………………………………1
1.1 前言…………………………………………………………1
1.2 研究動機與目的……………………………………………2
1.3 章節內容……………………………………………………4
第二章 相關技術回顧………………………………………………6
2.1 多輸入多輸出(MIMO)系統…………………………………6
2.1.1 空間多工(SM) MIMO系統…………………………8
2.1.2 空時區塊編碼(STBC) MIMO系統…………………9
2.1.3 混合空時區塊編碼-空間多工(Hybrid STBC-SM)MIMO系統…………………………………………………………………… 11
2.2 通道容量運算式……………………………………………14
2.3 基因演算法…………………………………………………16
2.3.1 二進制基因演算法…………………………………17
2.3.2 實數基因演算法……………………………………20
2.4 粒子群優化演算法…………………………………………22
第三章 PGA-RGA和PGA-PSO應用於天線選擇及功率分配………26
3.1 系統介紹……………………………………………………26
3.2 演算法整體架構……………………………………………29
3.2.1 PGA應用於天線選擇…………………………………31
3.2.2 RGA應用於功率分配…………………………………33
3.2.3 PSO應用於功率分配…………………………………35
3.3 模擬結果與討論……………………………………………36
第四章 討論及未來展望……………………………………………74
參考文獻………………………………………………………………75

參考文獻

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