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研究生:楊銘杰
研究生(外文):Ming-Jie Yang
論文名稱:非正交多工接取通訊下考慮錯誤遞移之排程與資源分配技術
論文名稱(外文):Loss-Aware Scheduling and Power Allocation for Non-Orthogonal Multiple Access
指導教授:謝宏昀
指導教授(外文):Hung-Yun Hsieh
口試委員:高榮鴻魏宏宇周俊廷
口試委員(外文):Rung-Hung GauHung-Yu WeiChun-Ting Chou
口試日期:2015-07-28
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:電信工程學研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:英文
論文頁數:69
中文關鍵詞:非正交多工無線接取排程及資源最佳化錯誤遞移
外文關鍵詞:NOMANon-orthogonal multiple accesserror propagationresource allocation and scheduling
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現行的無線通訊網路多以正交多工接取(OMA)為基礎技術以提供多用戶的服務,如在3GPP LTE及IEEE 802.16所採用的正交分頻多工接取(OFDMA)技術。然而,為因應未來對無線網路需求的大幅增加,必需進一步提昇頻譜的使用效率,因此非正交多工接取(NOMA)技術逐漸成為次世代無線通訊技術的焦點之一。在實現多工傳輸與OMA有許多本質上差異,NOMA能夠在同一資源方塊下排程多個使用者進行傳輸,而在接收端以疊代干擾消除(SIC)技術正確的解調資料。不同於過去相關文獻多停留在以理論的通道容量模型對NOMA的效能增益進行分析,本論文著眼於NOMA下涉及調變編碼、功率分配和錯誤傳播等在實作上需要考慮的因素,進而研究對資源方塊組下排程多個使用者的問題。為了解決採用非正交多元接取技術而衍生的問題,本研究首先建立鏈路級實體層模擬平台(Bit-Level Physical-LayerSimulator)以俾觀察上述因素對NOMA傳輸品質的影響。為求發揮在實際系統下帶來的效能提升,且在考慮到使用者之間的公平性前提下,我們首先提出一基於模擬的平台的混和整數及實數之非線性規劃問題。透過功率控制,最佳化地將使用者群組後分配至資源區塊中,本論文提出一以相對熵抽樣方法(Cross-Entropy method)為理論基礎之隨機搜尋演算法,以最大化系統之整體效能。我們提出以組合為基礎的抽樣方法(combination-based)以大幅提昇收斂的速度,比起一般的抽樣法(user-based)節省了41%的計算量。研究結果顯示提出之演算法在此一非定常多項式時間複雜度問題,能夠有效的獲得較好的排程解和其他系統設置最佳值。非正交多工接取技術可以達到比正交多工接取多出近100%的增益,而疊代排程演算法(iterative scheduling)以及空間區隔循序排程演算法(spatial-centric round robin),僅能達到我們提出的方法的92%及83%。若允許三個訊號源進行非正交多工傳輸,比起兩個增益能進一步提昇7.5%。以這些結果為根基,我們深入研究NOMA下考慮錯誤遞移之排程及資源分配演算法以及系統增益。

Existing wireless networks have predominantly adopted orthogonal multiple access such as OFDMA as the underlying multiple access technology. To further improve spectrum efficiency in next-generation wireless networks, however, a promising direction is to shift to non-orthogonal multiple access (NOMA). To address the technical challenges brought forth by NOMA, in this paper we investigate the problem of scheduling multiple NOMA users in the same resource block previously dedicated to a single user in OFDMA. Unlike related work that relies on the ideal link capacity model for profiling the performance gain of NOMA, we start by implementing a more practical NOMA simulator to investigate problems such as modulation/coding scheme (MCS) selection, power allocation, and error propagation along with transmission scheduling. We formulate an optimization problem for transmission scheduling with the goal of providing proportional fairness to the set of users served by a base station. We then propose a method to integrate the NOMA simulator into a meta-heuristic search algorithm for solving the joint optimization problem. Evaluation results show that the proposed scheduling algorithm can effectively solve the problem in the target scenario and NOMA can achieve significant performance benefits over OMA over 100% in throughput. Compared with other scheduling method such as iterative scheduling algorithm and spatial-centric round robin scheduling algorithm, these greedy approaches can only reaches 92% and 83% of proposed algorithm. We also found that the enabling 3-link NOMA can further improves performance 7% more than 2-link NOMA. Thus, with these evaluations, we motivates further investigation along this direction.

TABLE OF CONTENTS
ACKNOWLEDGEMENTS i
ABSTRACT (CHINESE) ii
ABSTRACT iii
LIST OF TABLES vi
LIST OF FIGURES vii
CHAPTER 1 INTRODUCTION 1
CHAPTER 2 BACKGROUND AND RELATED WORK 5
2.1 Theoretic Model of NOMA-SIC 5
2.2 Related Work 7
2.2.1 Non-Orthogonal Multiple Access 7
2.2.2 Time-Frequency Resource Allocation and Scheduling 8
2.2.3 Scheduling in NOMA scheme 9
CHAPTER 3 RESOURCE ALLOCATION IN NOMA 12
3.1 Network Scenario and Model 12
3.2 Problem Formulation 13
3.3 Expression of Bit-Error-Rate 16
CHAPTER 4 BIT-LEVEL PHYSICAL-LAYER SIMULATOR 19
4.1 Simulator Architecture 19
4.2 Simulator Settings 20
4.3 Bit-Error-Rate Characteristic of NOMA 23
4.4 Decoupling 24
CHAPTER 5 POWER ALLOCATION SUB-PROBLEM 27
5.1 Solving the Feasibility Problem 27
5.2 Initial State of Local Search 30
5.2.1 An Example of Analysis in Uncoded System 31
5.2.2 Approximation of BER in Quadrature Amplitude Modulation 33
5.2.3 Initial Point by Derivation of Uncoded System 34
5.3 Construction of MCS Map 35
CHAPTER 6 SCHEDULING SUB-PROBLEM 40
6.1 Cross Entropy Method 40
6.2 Solving the Scheduling Sub-Problem 43
6.2.1 Proposed Algorithm by Combination-Based Sampling 43
6.2.2 A Base-line Approache by User-Based Sampling 47
6.2.3 Discussion 48
6.3 Greedy Approaches to Scheduling 49
6.3.1 Iterative Greedy Scheduling 49
6.3.2 Spatial-Centric Round Robin Scheduling 50
6.4 Overall Algorithm 51
CHAPTER 7 PERFORMANCE EVALUATION 56
7.1 Performance of Power Allocation Algorithm 56
7.2 Performance of Scheduling 56
7.2.1 Pairing Users to NOMA 56
7.2.2 Improvement of System Performance 57
7.2.3 Convergence of the Algorithm 60
7.3 Proper Resolution Design for MCS Map 62
CHAPTER 8 CONCLUSION AND FUTURE WORK 65
REFERENCES 66

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