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研究生： 呂允騰
Yun-teng Lu
論文名稱： 以基因演算法探討於多使用者正交分頻多工系統之子載波、位元和功率配置機制
Genetic Algorithm Approach for Adaptive Subcarrier, Bit, and Power Allocation in OFDM System
指導教授： 陳永芳
Yung-Fang Chen
口試委員:
學位類別： 碩士
Master
系所名稱： 資訊電機學院 - 通訊工程學系在職專班
Executive Master of Communication Engineering
畢業學年度： 96
語文別： 中文
論文頁數： 60
中文關鍵詞： 幅度調適最佳化 、基因演算法 、進化策略
外文關鍵詞： genetic algorithm, margin adaptive optimization, evolution strategy
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    • 本論文目的驗證基因演算法(GA)應用在正交多工系統功率消耗最佳的幅度調適最佳化，問題探討下，可以克服一般最佳化機制容易陷入區域最佳情形，並持續就基因演算法是否可以與進化策略(ES)演算法，有相同功率消耗最佳解的表現進行研討。
    論文中以建設性通道配置(CIA)及動態通道配置(DCA)作為一般功率消耗最佳化機制的比較對象，基因演算法(GA)及進化策略(ES)演算法作為相對比較對象，另就基因演算法及進化策略演算法同時加入Fuzzy Logic Controller(簡稱 FLC)和延伸機制，除觀察是否較原有演算法可獲得較佳表現外，並觀察基因演算法相對求功率消耗最低的表現。
    模擬結果除證實基因演算法和進化策略演算法，較一般演算法求功率消耗最低表現為佳外，基因演算法和進化策略演算法加入FLC和延伸機制後，除較原先機制有較佳表現外，相同條件下，基因演算法在特定的基因長度下和進化策略演算法功率消耗求解表現相當。

    In this thesis, we proof Genetic Algorithm(GA) and Evolution strategy(ES) performance better than general suboptimal algorithms(Constructive Initial assignment and Dynamic Channel Allocation) in Margin Adaptive Optimization(MA) problem based on OFDM communication system, cause GA and ES algorithms can jump out of local minimum easier than general suboptimal algorithms.
    We continue to compare with GA and ES algorithms in both of them add Fuzzy Logic Controller(FLC) and extension method. Simulation results show that, GA and ES get better performance than before, and in the same condition GA has the same performance with ES in specific gene length setting.

    中文提要 ……………………………………………………………… i 英文提要 ……………………………………………………………… ii 誌謝 ……………………………………………………………… iii 目錄 ……………………………………………………………… iv 圖目錄 ……………………………………………………………… vi 表目錄 ……………………………………………………………… viii 符號說明 ……………………………………………………………… ix 一、 研究背景與動機…………………………………………… 1 1-1 研究背景…………………………………………………… 1 1-2 研究動機…………………………………………………… 2 二、 系統架構介紹……………………………………………… 4 2-1 OFDM 簡介………………………………………………… 4 2-1-1 OFDM 歷史………………………………………………… 4 2-1-2 OFDM 技術簡介…………………………………………… 4 2-1-3 系統模型參數設定………………………………………… 5 2-2 通道模型參數設定………………………………………… 7 2-2-1 通道特性…………………………………………………… 7 2-2-2 通道模型參數設定………………………………………… 10 三、 資源配置模型及問題設定………………………………… 12 3-1 資源配置模型說明………………………………………… 12 3-2 資源配置問題……………………………………………… 13 四、 基因演算法原理介紹……………………………………… 14 4-1 GA 演算法簡介…………………………………………… 14 4-2 GA 演算法理論說明……………………………………… 14 4-2-1 初始族群(Initial Population)……………………………… 15 4-2-2 選擇(Selection)…………………………………………… 16 4-2-3 交配(Crossover)…………………………………………… 16 4-2-4 突變(Mutation)…………………………………………… 23 4-2-5 下ㄧ世代的產生(Next Generation)……………………… 23 五、 加入基因演算法之資源配置機制………………………… 24 5-1 初始族群(Initial population)產生………………………… 26 5-2 以CIA 機制爲初始族群增加良好的染色體……………… 29 5-3 使用填水式進行位元負載………………………………… 30 5-4 評估(Evaluate)……………………………………………… 31 5-5 交配(Crossover)…………………………………………… 32 5-6 突變(Mutation)……………………………………………… 33 5-7 下ㄧ世代的產生…………………………………………… 35 5-8 延伸機制…………………………………………………… 35 六、 模擬與討論………………………………………………… 38 6-1 通道(channel)及系統模型參數設定……………………… 38 6-2 基因演算法參數設定……………………………………… 40 6-3 資源配置機制演算法結果比較…………………………… 41 七、 結論………………………………………………………… 57 參考文獻 ……………………………………………………………… 59

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