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研究生:陳柏安
研究生(外文):Bor-An Chen
論文名稱:演化式演算法於D2D系統資源分配最佳化之應用研究
論文名稱(外文):Performance Optimization of Resource Allocation in D2D System Using Evolutionary Algorithms
指導教授:黃永發黃永發引用關係譚旦旭譚旦旭引用關係
指導教授(外文):Tan-Hsu TanTan-Hsu Tan
口試委員:林進豐張正春
口試日期:2016-07-19
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:電機工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
畢業學年度:104
語文別:中文
中文關鍵詞:基因演算法粒子群演算法演化式演算法資源分配LTE系統裝置對裝置通訊
外文關鍵詞:Genetic Algorithm (GA)Particle Swarm Optimization (PSO)Evolutional AlgorithmsResource AllocationLong Term Evolution (LTE)Device-to-Device (D2D)
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裝置對裝置通訊(Device-to-Device, D2D)無線通訊技術為下一代行動通訊的重點發展技術,可提高無線網路系統資源的使用效率,本研究探討D2D技術於長期演進技術(Long Term Evolution, LTE)系統中的資源塊(Resource Block, RB)之分配(Allocation)議題,除考慮系統之用戶數容量(Number of Users Capacity)效能外,系統傳輸量(System Throughput)也是重要效能。我們應用粒子群演算法(Particle Swarm Optimization, PSO)與基因演算法(Genetic Algorithms, GA)以找出系統效能最佳化之RB分配。模擬結果顯示,本文提出之改良型粒子群演算法(Refined PSO, RPSO)在五個族群數目(Population)時,即可於100代(Generation)內收斂到次佳解(Sub-optimal Solution),節省了大量的運算量,而GA在100個族群數目及100代時,其效能趨近全域最佳解(Globel Optimal Solution)。因此,本文提出之RPSO及GA演化演算法(Evolutional Algorithms),與全面搜尋法(Exhaustive Search Method)相比,均可節省大量的運算量,並獲得次佳的系統效能。
The Device-to-Device (D2D) is a developing key technique for next generation mobile communication systems. The D2D technique allows unauthorized user equipment (UE) to access unused authorized cellular UE band, thereby improves the utilization efficiency of bandwidth resource. In this study, the resource blocks (RB) are allocated to UEs according to the evolutional algorithms for long term evolution (LTE) systems. We proposed evolutionary alogorithms for RB allocation to enhance the system throughput and the number of UEs in system capacity performance. Both the Refined Particle Swarm Optimization (RPSO) and Genetic Algorithm (GA) are proposed to investigate the optimal system performance. The simulation results show that with less population size of M=5, the RPSO can perform quickly covergency to sub-optimal solution in the 100th generation. Moreover, with moderate population size of M=100, the GA can achieve nearly global optimal solution in the 100th generation. Therefore, as compared to the exhaustive search method, both proposed RPSO and GA can obtain sub-optimum performance with less computational complexity for D2D mobile communication systems.
摘要 i
ABSTRACT ii
目錄 iii
表目錄 v
圖目錄 vi
第一章 緒論 1
1.1 研究背景 1
1.2 研究方法與目的 3
1.3 論文架構 3
第二章 長期演進技術(LTE) 4
2.1 簡介 4
2.2 SC-FDMA介紹 4
2.3 資源塊 7
第三章 運用中繼站之裝置對裝置通訊資源分配 9
3.1 簡介 9
3.2 模擬環境 9
3.2.1 系統模型 9
3.2.2 模擬參數 13
3.3 模擬與驗證 15
3.4 可達到的系統容量 19
第四章 基於粒子群演算法之資源塊最佳分配 21
4.1 最佳化演算法 21
4.2 粒子群演算法 21
4.2.1 演算法簡介 21
4.2.2 應用粒子群演算法執行資源塊分配之最佳化 21
4.2.3 原始粒子群演算法 22
4.2.4 簡化型粒子群演算法之詳細執行流程 22
4.3 模擬結果 25
4.3.1 模擬參數之設定 25
4.3.2 以SPSO執行系統容量之最大化 28
4.3.3 粒子群演算法之改善(RPSO) 30
4.3.4 在盡可能保證系統容量的條件下以RPSO提升傳輸量 34
4.3.5 不同類型用戶之優先度 38
4.3.6 總結與討論 39
第五章 基於基因演算法之資源塊最佳分配 41
5.1 基因演算法簡介 41
5.1.1 基因演算法之執行流程 41
5.1.2 編碼 42
5.1.3 初始化染色體族群 42
5.1.4 適應性數值之計算 42
5.1.5 選擇 43
5.1.6 交配 44
5.1.7 突變 45
5.2 模擬結果 46
5.2.1 參數之設置 46
5.2.2 GA與RPSO所獲效能之比較 49
5.3 運算量計算 50
第六章 結論 52
參考文獻 53
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