臺灣博碩士論文加值系統

隨著用戶及傳輸量需求的增長，提高頻譜效益的課題也越趨重要。而非正交多重存取技術 (NOMA) 因為能夠有效提高頻譜資源的使用率，因此被視為未來行動通訊網路中重要的技術之一。在本篇論文中，我們將基地台與使用者之間的互動看成是一種Stackelberg game，而主要目標為最大化滿足服務品質的使用者數量，次要目標則是最大化基地台的整體利潤。為了達成目的，我們提出了一個包含功率分配及資源區塊分配的演算法。對於每一組已知的NOMA配對，我們推導出一個公式能夠用來解決功率分配的問題。另外，我們將資源區塊分配問題轉換成在弦圖中找出最大權重及獨立集的問題，並且使用線性時間的演算法找到解答。而實驗結果顯示我們提出的演算法在滿足服務品質的使用者數量以及系統整體的傳輸速率都明顯優於其他我們拿來比較的演算法。

Non-orthogonal multiple access (NOMA) has been considered as a promising radio access technique for the future mobile networks due to its superior spectrum efficiency. In this thesis, we consider a downlink NOMA system. We model the interaction between the base station and multiple users as a Stackelberg game and then propose a fast resource allocation algorithm, which consists of power allocation and resource allocation, to maximize the number of satisfied user equipment while seeking to enhance the revenue of the base station. Given a NOMA pair, we derive a closed-form solution for the optimal power allocation. And we convert the resource allocation problem into the problem of finding a maximum weight independent set in a chordal graph and thus can use a linear-time algorithm to find a maximum weight independent set. Simulation results show that proposed fast resource allocation algorithm outperforms the compared NOMA algorithms in terms of the number of satisfied user equipment and the system total throughput.

Acknowledgement iii
Abstract iv
中文摘要 v
Table of contents vi
List of figures ix
Chapter 1 Introduction 1
1.1 Concept of NOMA 1
1.2 Superposition coding (SC) 3
1.3 Successive interference cancellation (SIC) 4
Chapter 2 Related work 5
Chapter 3 System model 9
3.1 Two-UEs NOMA scheme 9
3.2 Problem formulation 11
3.3 Successful Decoding Probability in NOMA 12
Chapter 4 Power allocation 15
4.1 Revenue-based Power Allocation method 16
Chapter 5 Resource block allocation 19
5.1 Partner Selection 20
5.2 Resource Block Allocation 27
5.3 Proof of Chordality 30
5.4 Complexity Analysis 39
Chapter 6 Simulation 41
6.1 Compared Algorithm 41
6.1.1 Maximum Weighted Maximum Cardinality Matching Algorithm 42
6.1.2 Iterative Full Search and Maximum Weighted Independent Set Algorithm 43
6.1.3 Iterative Maximum Weighted Independent Set Algorithm 43
6.1.4 Distanced-Based Algorithm 44
6.1.5 Channel State Sorting Pairing Algorithm 44
6.1.6 Population-based Meta-heuristic Algorithm 45
6.1.7 Hungarian-based Pairing Algorithm 47
6.1.8 Optimal OMA Resource Allocation Algorithm 47
6.2 Simulation settings 48
6.3 Simulation results 49
6.3.1 Different limit L in Iterative BS+MWIS Algorithm 50
6.3.2 Three Scenarios in Iterative BS+MWIS Algorithm 52
6.3.3 Performance Evaluation of Iterative BS+MWIS Algorithm 57
6.3.4 The Comparison between Iterative BS+MWIS algorithm and other algorithms 61
Chapter 7 Conclusion 66
Reference 67