為了滿足未來行動網路傳輸需求的增加和行動裝置的增長，非正交多重存取技術 (NOMA) 被視為具有潛力的技術之一。非正交多重存取技術不僅可以提升通道狀況品質較差之使用者的傳輸速率，也更充分的運用頻譜資源，因此，本篇論文考量了非正交多重存取技術支援多基地台聯合傳輸協作技術 (JT-CoMP)，改善基地台邊緣使用者的服務品質，以提升整體系統表現。此篇論文主要目的為最大化符合傳輸品質的使用者數量，次目的為最大化整體系統的傳輸速率，在考量下行鏈路NOMA傳輸的情形下，首先我們推導出在JT-CoMP技術支援的情況時，使用者所需功率以滿足服務品質的公式解，接著使用所求的結果，將其套用在資源區塊分配演算法中，我們將使用者分組問題轉換成一對一指定問題和尋找最大權重獨立集問題，以此來達到我們的目的。模擬結果顯示我們所提出的演算法不管在整體系統傳輸速率或達到傳輸品質使用者數量的表現都優於其他比較的演算法。

To achieve dramatically increasing demand for data transmission in next-generation mobile networks, non-orthogonal multiple access (NOMA) has been considered as one of the key techniques. NOMA not only improves the data rate for users with poor channel quality but also enhances spectrum efficiency. To elevate the throughput of cell-edge users, we also take the joint transmission coordinated multipoint technique (JT-CoMP) into account in our system. In this thesis, the primary goal is to maximize the number of well-served users and the secondary goal is to increase system throughput. Consider a downlink NOMA system with JT-CoMP. We first derived a closed-form solution for the optimal transmission power values, given either a user pair or a user triple. After that, we utilize the result of optimal power allocation to maximize the number of well-server users. We transform the user grouping problem into a problem of finding the maximum weight independent set of a graph. Simulation results present that our proposed resource allocation algorithm is superior to several existing NOMA algorithms in terms of both the number of well-served users and the system sum rate.

Acknowledgement ii
Abstract iii
摘要 iv
Table of Contents v
List of Figures vii
Chapter 1. Introduction 1
1.1 Concept of NOMA 2
1.2 Superposition Coding (SC) 4
1.3 Successive interference cancellation (SIC) 5
1.4 Coordinated Multipoint (CoMP) 6
Chapter 2. Related Work 7
Chapter 3. System Model 11
3.1 Intra-Cell NOMA Scheme 12
3.2 NOMA with JT-CoMP Scheme 13
3.3 NOMA with Borrowed JT-CoMP Scheme 15
3.4 Problem Formulation 17
Chapter 4. Power Control 19
4.1 Block Successive Upper-Bound Approach 21
Sub Problem α 22
Sub Problem β 26
4.2 Initial Point of BSUM Power Control 30
Chapter 5. Resource Block Allocation 43
Step 1. 44
Step 2. 45
Step 3. 46
Chapter 6. Performance Evaluation 52
6.1 Compared Algorithms 52
6.2 Simulation Setting 56
6.3 Simulation Results 57
Chapter 7. Conclusion 65
Reference 66

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