為了因應次世代大量行動裝置而造成的傳輸量需求，下行鏈路的非正交多頻分址技術 (NOMA) 被視為是一個重要的解決方案，有別於傳統只能將資源按照時間或頻率切割成資源區塊後，分配給單一用戶的正交多頻分址技術，非正交多頻分址技術藉由疊加編碼及連續干擾消除法的技術，可使用單一的資源區塊來服務多個用戶以增加頻譜效益。為了非常貼近實際行動網路的情況，本篇論文的系統模型中，我們考慮了用戶端非零的中斷概率及異質的資源區塊。在本篇論文中，我們提出了一個結合用戶配對與功率分配的演算法，主要目的為最大化滿足服務品質的使用者數量，次要目的是最大化系統整體傳輸速率。在模擬結果中，可以顯示出我們在滿足服務品質的使用者數量上，明顯優於其他與我們比較的演算法。

In order to meet the explosive demand incurred by mobile applications, the downlink non-orthogonal multiple access (NOMA) has been regarded as a promising solution. Different from conventional orthogonal multiple access (OMA) technologies that different users are allocated to distinct orthogonal resources in either time or frequency domain, NOMA, which multiplexes multiple users in the same frequency resource, can enhance spectrum efficiency with the help of the use of superposition coding (SC) and successive interference cancellation (SIC). To be very practical, our system model takes non-zero outage probability at UEs and heterogeneity of resource blocks into account. We propose a joint user pairing and power allocation algorithm whose primary objective is to maximize the number of satisfied users and whose secondary objective is to maximize sum rate. Simulation results indicate that the proposed algorithm outperforms the compared algorithms significantly in terms of the number of satisfied users.

Acknowledgements iii
Abstract iv
中文摘要 v
Table of Contents vi
List of Figures ix
Chapter 1. Introduction 1
1.1 Non-Orthogonal Multiple Access in Downlink 2
1.2 Non-Orthogonal Multiple Access in Uplink 4
1.3 Successive Interference Cancellation (SIC) 5
Chapter 2. Related Work 8
Chapter 3. System Model 14
3.1 Path Loss Model 14
3.2 NOMA Scheme 18
3.3 Successful Decoding Probability in NOMA 20
Chapter 4. Power and Resource Allocation Algorithm 24
4.1 Power Allocation 24
4.1.1 Block Successive Upper-bound Method 26
4.2 Resource Block Allocation 29
4.2.1 The Concept of Chordal Graph 31
4.2.2 Maximum Cardinality Search Algorithm 32
4.2.3 Fill-in Algorithm 34
4.2.4 Iterative Maximum Weighted Independent Set Algorithm 39
4.2.5 OMA Scheme 43
Chapter 5. Performance Evaluation 44
5.1 Compared Algorithm 44
5.1.1 Maximum Weighted Maximum Cardinality Matching OMA Algorithm 45
5.1.2 Distanced-Based NOMA Algorithm 46
5.1.3 Channel State Sorting-pairing NOMA algorithm 47
5.1.4 Population-based Meta-heuristic NOMA Algorithm 48
5.2 Simulation Setting 50
5.3 Simulation Results 51
5.3.1 Lower Rate Demand Scenario 52
5.3.2 Higher Rate Demand Scenario 57
Chapter 6. Conclusion 62
Bibliography 63

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