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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

使用調適性的CoMP於LTE-A Downlink端提升頻譜的使用率 / Hierarchical Adaptive Clustering for CoMP in LTE-A Downlink Transmission to Improve the Spectrum Efficiency

蔡欣儒, Tsai, Hsin Ju Unknown Date (has links)
第四代行動通訊系統(The Fourth Generation of Mobile Communications System,簡稱4G)LTE-A(Long Term Evolution-Advanced)利用載波聚合(Carrier Aggregation)與多天線MIMO(Multi-Input Multi-Output)通道技術大幅提升上傳與下載的傳輸速率,並加入協同多點協調傳輸(Co-ordinated Multi-Point Transmission)技術加強基地台服務的覆蓋率。透過LTE-A的CoMP聯合運作(Joint Processing)方式,藉由鄰近基地台之間的互相協助,有助於位於細胞邊緣處之使用者裝置(User Equipment,UE)訊號傳輸品質提升,將周圍鄰近之基地台訊號的干擾化為有益之訊號來源。中繼技術(Relay)則能將來自基地台之無線電訊號接收後經過解碼與編碼再送出,提升周遭UE接收的訊號強度。 基於行動網路環境中使用者的移動性,細胞邊緣使用者的人數與位置分布隨時間改變,傳統CoMP傳輸多屬靜態的叢集演算法事先定義CoMP傳輸叢集,導致傳輸叢集不符合細胞邊緣使用者的分布與需求,細胞邊緣使用者的傳輸增益有限。動態的CoMP傳輸雖然較靜態的CoMP傳輸符合邊緣使用者的需求與分布,然而,因其屬於分散式的架構缺乏管理控制中心,規劃傳輸叢集的過程需仰賴基地台之間頻繁的控制訊號溝通。 本論文提出一個動態的CoMP傳輸叢集演算法-階層式動態CoMP傳輸叢集演算法(Hierarchical Adaptive Clustering for CoMP ,HACC),透過階層式架構,不但具備靜態CoMP傳輸演算法集中式系統的優點,也保有動態CoMP傳輸演算法隨使用者分布調整傳輸叢集的特點。首先於系統定義之叢集中選出上層叢集代表(top cluster head,TCH),由基地台收集服務範圍內UE分布與通訊品質,篩選出細胞邊緣使用者並傳遞此資訊給TCH,由TCH選出較多細胞邊緣使用者的區域為CoMP傳輸叢集之子代表(sub-cluster head),以CoMP傳輸叢集之子代表為中心點尋找相鄰的區域形成CoMP傳輸叢集。除此之外,再搭配Relay延伸來自基地台之訊號,強化基地台服務範圍內非邊緣區域之訊號,提供UE更佳的傳輸品質。 透過實驗模擬證實,本論文提出的方法在系統整體UE的資料吞吐量比傳統靜態以及Hongbin et al.[10]提出之以UE需求為主的動態CoMP叢集演算法來的優異,特別是對位於細胞邊緣通訊不良處之UE資料吞吐量有更顯著之改善,系統整體的頻譜效率也有所提升。 / The fourth-generation mobile communications system (4G) LTE-A (Long Term Evolution-Advanced) uses carrier aggregation and multi-antenna MIMO channel technology dramatically to increase the speed in both uplink and downlink, and use coordinated multi-point transmission(CoMP) and relay to improve the coverage of base station. Through joint processing(JP) in CoMP, base station(BS) communicates with adjacent BSs and then some of them build up a CoMP cluster helping the user equipment(UE) which is located at the edge of cell by enhancing the signal strength. CoMP-JP is able to transform interference from adjacent cells into useful signals. Relay technology receives radio signals and then amplifies signals before re-transmission to strengthen signals. The number of cell-edge users and their locations change with time due to the mobility of users in mobile communications system. Most traditional static CoMP transmission clustering algorithm are predefined CoMP clusters. As the distribution of cell-edge users in the system changes, the transmission clusters may not meet the needs of cell edge UEs so that the transmission gain is limited. Compared with static CoMP clustering, dynamic CoMP clustering changes with time to meet the needs of cell-edge UEs, providing an appropriate service to cell-edge UEs. However, dynamic system belongs to distributed system and lacks management control center, it highly depends on frequent communication signals among base stations during the process of clustering generation. This paper proposes a dynamic clustering algorithm for CoMP-JP - Hierarchical Adaptive Clustering for CoMP (HACC). By hierarchical structure, HACC not only has the advantages of static CoMP centralized system, but also maintains the characteristics of dynamic CoMP adjusting the clustering with cell-edge users. At the first step, we define an upper cluster representative of the group (top cluster head). Then, depending on the number of cell-edge UEs in every sector, the system chooses sub-cluster head. Sub-cluster head chooses neighboring sectors to generate a CoMP-JP transmission cluster. In addition, relay stations amplify the signal from BS providing better transmission quality for non-cell-edge UEs. Simulation results show that the proposed method outperforms traditional static CoMP clustering and UE-specific CoMP clustering method proposed by Hongbin et al.[10] in data throughput, particularly for cell-edge UEs, and spectrum utilization.

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