完整後設資料紀錄
DC 欄位語言
dc.contributor.author黃鈺涵en_US
dc.contributor.authorHuang, Yu-Hanen_US
dc.contributor.author趙禧綠en_US
dc.contributor.authorChao, Hsi-Luen_US
dc.date.accessioned2014-12-12T02:38:37Z-
dc.date.available2014-12-12T02:38:37Z-
dc.date.issued2013en_US
dc.identifier.urihttp://140.113.39.130/cdrfb3/record/nctu/#GT070056536en_US
dc.identifier.urihttp://hdl.handle.net/11536/73679-
dc.description.abstract近年來,由於無線通訊產品推陳出新,手持裝置的數量將會持續上升,因此未來的無線系統將會需要更多資源。為了因應未來的趨勢,我們利用感知無線電網路 (Cognitive Radio Network) 及雲端來分配及管理巨大的資源。 在我們的論文中,我們設計了一個運作在電視空白頻譜上的感知無線電雲端網路(cloud-based cognitive radio access network,C2-RAN)。為了有效利用電視空白頻譜的資源,我們提出了一個適用於我們系統上的資源管理架構。我們的資源管理架構主要分成三個部分,包括在雲端上的分群及資源管理、在雲端上的功率控制及資源分配、以及在感知無線電存取點上的資源管理及使用者排程。在本論文中,我們集中在第三部分。具體來說,我們定義一些服務類別,將使用者分為三種狀態,來保障移動使用者服務的連續性,我們將相同狀態的使用者分成幾個群組,並提出一個資源對應方法將使用者的需求轉換成所需頻道數。在雲端完成資源管理前兩層的資源分配及功率控制後,我們設計的排程演算法將進一步以時間區塊為單位分配資源給使用者,並最大化系統的效能。 為了解決這個問題,我們提出了一個最佳化演算法,但由於最佳化演算法複雜度過高,因此,我們提出了一個複雜度較低的排程演算法套用到我們系統上。最後,從小規模模擬結果中可以看出,在效能上,我們提出的演算法與最佳化演算法的比較,以及我們設計用來保障不同服務使用者的限制式,對整體效能所帶來的影響。而從大規模的模擬中,我們可以看出一整套系統從端至雲,再由雲至端為使用者帶來的效益。zh_TW
dc.description.abstractIn recent years, because of the wireless communication product innovation, the number of handheld devices will continuously increase, so the future wireless systems will require more resources. In response to the trend of the future, we use Cognitive Radio Network and a cloud to allocate and manage huge data. We have designed cloud-based cognitive radio access network (C2-RAN) in TV white space in our works. To effectively use the resources, we proposed a resource management scheme for our C2-RAN. Our resource management scheme is separated to three parts, clustering and resource management in Cloud, power control and channel allocation in Cloud, and resource management and user scheduling in CR access points (CR APs). This paper focuses on the third part. Specifically, we define several service classes. To protect continuity of service for mobile users, we define three states for users and group the users with the same state. We propose a physical channel mapping method to change the service request into the number of required channels. After the first two-tiers channel allocation and power control mechanisms performed at the cloud, the designed scheduling algorithm further allocates resources (in terms of time slots) to CR users to maximize the sum of throughout utilities. To solve this problem, we proposed an optimal algorithm. However, because of the high time complexity of optimal algorithm, we proposed a heuristic scheduling algorithm for our system. Finally, the small-scale simulation results show the comparison in the performance between optimal algorithm and our proposed algorithm. In addition, the small-scale simulation results also show the impact of overall performance with changing the service guarantee degree and utilization degree. From the large-scale simulations, we can see the overall benefits of the users in our system.en_US
dc.language.isozh_TWen_US
dc.subject自組織網路zh_TW
dc.subject使用者排程zh_TW
dc.subject服務品質zh_TW
dc.subjectSelf-Organizing Networksen_US
dc.subjectUser Schedulingen_US
dc.subjectQoSen_US
dc.title行動自組織網路中支援服務品質的使用者排程機制zh_TW
dc.titleUser Scheduling with QoS Support in Mobile Self-Organizing Networksen_US
dc.typeThesisen_US
dc.contributor.department網路工程研究所zh_TW
顯示於類別:畢業論文