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研究生: 蔡佩伶
Tsai, Pei-Ling
論文名稱: 於LTE-Advanced網路中考慮載波聚合技術及多天線技術的下行無線資源分配演算法
Downlink Radio Resource Allocation with Carrier Aggregation and MIMO in LTE-Advanced Networks
指導教授: 陳文村
Chen, Wen-Tsuen
林靖茹
Lin, Ching-Ju
口試委員: 陳文村
Chen, Wen-Tsuen
林靖茹
Lin, Ching-Ju
許健平
Sheu, Jang-Ping
學位類別: 碩士
Master
系所名稱: 電機資訊學院 - 資訊工程學系
Computer Science
論文出版年: 2013
畢業學年度: 101
語文別: 英文
論文頁數: 49
中文關鍵詞: 傳輸區塊多輸入多輸出長期演進技術升級版載波聚合技術無線資源分配
外文關鍵詞: Transport Block, MIMO, LTE-A, Carrier Aggregation, Radio Resource Allocation
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  • 有鑑於用戶端的頻寬需求與日俱增,新一代無線通訊系統LTE-A(Long Term Evolution-Advanced)提出載波聚合(Carrier Aggregation)技術,使行動裝置能同時聚合多個分量載波(component carrier),以達成更高的傳輸速率。隨著多天線技術的發展,目前無線通訊系統中的傳輸端及接收端皆已配置多天線,藉由多天線的各種技術進一步提升傳輸速率及傳輸品質。載波聚合及多天線技術將會大幅影響每個用戶端的通道狀況,然而目前探討無線資源分配的研究中,皆未在載波聚合及多天線技術並存的系統中作討論。因此此篇論文是第一篇將載波聚合及多天線技術考慮進無線資源分配的論文。
    在此篇論文中,我們考慮每個用戶端的傳送需求為一個有限佇列,在此狀況下視通道狀況將每個分量載波上的資源區塊(resource block)配置給多個用戶端。在分配完資源區塊後,我們接著必須決定此資源區塊所使用的調變與編碼方法,LTE-A的硬體設計規範在同個分量載波上分配給同個用戶端的資源區塊必須在同個數據流上使用共同的調變與編碼方法。這篇論文所要解決的問題是屬於NP-Hard難題,因此我們提出一個貪婪演算法試圖盡可能最大化吞吐量及盡量維持用戶端公平的原則下解決此問題。我們在文中證明此方法保證至少達到最佳解的一半效能。實驗模擬結果顯示,此方法的效能在各種情境下皆優於其他相關研究提出的方法。


    1 Introduction 1 2 Background and Formulation 5 2.1 LTE/LTE-A frame structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 MIMO modes with CA configuration . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.3 Downlink system model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.4 Problem formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.5 Backlogged traffic model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.6 Finite Queue model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.7 Hardness Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3 Proposed Scheme 16 3.1 Proposed Greedy Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 3.2 Optimal RB-Selection and bit allocation . . . . . . . . . . . . . . . . . . . . . . . 19 i3.3 Computational complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.4 Performance analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 4 Simulation 34 4.1 Simulation settings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 4.2 Simulation results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 4.2.1 Backlogged traffic model . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 4.2.2 Finite queue model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 5 Conclusions 45

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