隨著行動網路需求日益的增加，需求也成指數的增長，行動網路造成的能源消耗變高。因此雲端無線電接入網路(Cloud Radio Access Network, C-RAN)被提出來，將基地台(Macro Base Station, MBS)架構拆分成基頻單元 (Baseband Unit, BBU)與遠端無線接頭(Remote Radio Head, RRH)，多個基頻單元將會構成基頻單元池(Baseband Unit pool, BBU pool)，而基頻單元池透過前傳(Fronthaul) 連結無線寬頻頭端設備，此架構可以集中處理單元達到節能效果。
但因為網路架構為演進式架構，會隨著使用者的需求擴建RRH與BBU pool，因此大規模的RRH與BBU pool環境中如何有效率的分配資源成為一個重大的議題，基於上述本文採用C-RAN的技術提出多個BBU pool的架構，並且考慮在動態環境中BBU pool的休眠機制達到節省能源消耗，但只考慮能源損耗會導致RRH連結過長的BBU pool，而過長的連結會增加傳輸延遲時間，此外為了降低能源損耗與傳輸延遲也必須重新建立RRH與BBU pool的連結導致頻繁的換手會導致信令的註冊延遲，因此將問題定義成多目標問題，並且透過啟發式演算法中的模擬退火與基因演算法計算最適合的資源分配。模擬結果顯示基因演算法可以節省更多能源消耗與延遲時間，模擬退火演算法可以在較少的換手次數下保有不錯的能源消耗與延遲時間。

The increased number of End-devices has caused explosive growth in the transmission requirements of mobile networks and power consumption. The C-RAN which splits the traditional base station into Remote Radio Head (RRH) and Base Band Unit (BBU) and fronthaul is proposed as a potential architecture to reducing power consumption. The network is an evolution architecture. It will build more BBU pools and RRHs when the users are increasing. How to make resource allocation efficient becomes a major issue. This thesis proposed the C-RAN for multiple RRHs to multiple BBU Pools and considered the BBU pools with sleep mechanism. But only considering power consumption will cause long fronthaul and delay time. Although establishing connection with RRHs and BBU pools can reduce power consumption and delay time, it will cause frequency handover and take more signaling registration delay time. This thesis defines a multi-objective optimization problem and optimizes the best resource allocation with simulated annealing algorithm (SA) and genetic algorithm (GA) of the heuristic algorithm. The simulation results show that the GA can reduce more power consumption and delay time, and the SA can maintain good power consumption and delay time with fewer handover.

第一章 緒論 1
1.1研究背景 1
1.2研究動機 2
1.3 論文架構 3
第二章 背景知識 5
2.1 雲端無線電接取網路 5
2.2 相關研究 6
2.3多重遠端無線接頭與多重基頻單元池的雲端無線接取網路 9
第三章 問題定義 11
3.1 網路環境 12
3.2 能源消耗模型 14
3.3 換手次數模型 15
3.4 傳輸延遲模型 16
3.5多目標優化公式與限制式 17
第四章 提出方法 21
4.1 動態裝箱問題 21
4.2 基於模擬退火資源分配演算法(RABSA) 23
4.3 基於基因資源分配演算法(RABGA) 26
第五章 實驗模擬 31
5.1 模擬環境設置 31
5.2 模擬結果 35
第六章 結論與未來展望 47
參考文獻 49

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