臺灣博碩士論文加值系統

在本篇論文中，我們調查非正交多工接取 (NOMA) 之子平頻帶公平排程技 術並實作。非正交多工的核心概念是在發射端波束中重疊多個用戶的信號，並在 接收端使用連續消除的技巧解開信號。為了研究 NOMA 的效能表現，我們將此概 念實作在維也納鏈級模擬器和系統級模擬器上。我們首先呈現修改了哪些維也納 模擬器中的模型，使得 NOMA 技術能在其環境下實行。在鏈級模擬器中，我們得 以研究不同功率和不同的調變與編碼方式所對應的資料傳輸量。在系統級模擬器 中，則得以如 LTE 標準下傳輸資料並研究排程技術。在同時考慮公平性與整體的 傳輸量下，我們採用比例公平調度的模型作為最佳化的目標函數。我們首先提出 了基於交叉熵的觀念建立的啟發式排程演算法來解決最佳化的資源分配問題。接 下來，我們同時考慮到在 LTE 標準下，下行控制資訊必須要做到哪些調整。在我 們的模擬結果中，提出的 NOMA 架構相比於傳統的正交方式能達到更佳的公平性， 同時在整體的資料傳輸量上能夠達到更好的效益。在寬頻排程使用時，整體的資 料量可以上上升到 25%而在使用子頻帶排程時，則可以上升到 29%的效能。我們 提出的演算法相比於全區域搜尋演算法能節省超過 50%的執行時間，相比於貪婪 式演算法則有 5%的總資料量增益。基於這些結果，我們進一步討論了使用 NOMA 之子頻帶排程技術的權衡與效益。

In this thesis, we investigate subband allocation for proportionally fair schedul- ing for non-orthogonal multiple acess (NOMA). The key concept of NOMA is to transmit composition of multi-user signals in a beam at the transmitter and de- code with successive interfernece cancellation (SIC) at the reciever. We simulate NOMA to investigate perfromance with the Vienna link-level simulator and the Vienna system-level simulator. We first present how to modify the structure of Vienna simulator in order to build a NOMA simulation environment. In the link-level simulator, we can have the real performance to investigate the impact of power ratio modulation coding scheme. In the system-level simulator, we simulate with the LTE standard-compliant environment which implemented with iterative scheduling and subband allocation to research the e↵ect of scheduling algorithms. For the sake of maximizing total throughput and fairness, the proportionally fair (PF) scheduling model is adopted to evaluate system performance. We propose a meta-heuristic algorithm based on cross entropy method to solve the optimiza- tion problem for subband allocation PF scheduling. In addition, downlink control information constraints for scheduling in realistic system such as LTE/LTE-A is also taken into account. Simulation results show that NOMA can achieve higer fariness and performance benifits over OMA about 25% for wideband scheduling and 29% for subband scheduling. Compared with other subband allocation al- gorithm, the proposed algorithm have more than 50% lower executing time than full-search and 5% performance gain of greedy approach. With these results, we further discuss benefits and trade-o↵ of NOMA with subband scheduling.

ABSTRACT .................................. ii
LISTOFTABLES .............................. v
LISTOFFIGURES ............................. vi
CHAPTER1 INTRODUCTION .................... 1
CHAPTER 2 BACKGROUND AND RELATED WORK . . . . . 4
2.1 NOMA with SIC System ....................... 4
2.2 Vienna Simulator ........................... 5
2.2.1 Vienna Link-level Simulator ................. 6
2.2.2 Vienna System-level Simulator ................ 7
2.3 Related Work ............................. 8
2.3.1 Application of the Vienna LTE Simulator. . . . . . . . . . 8
2.3.2 Non-orthogonal multiple access................ 9
2.3.3 Scheduling in NOMA scheme................. 10
CHAPTER 3 MODIFIED MODEL FOR NOMA UNDER SUBBAND IN VIENNA SIMULATOR.................. 12
3.1 Extension of Vienna Link Level Simulator . . . . . . . . . . . . . . 12
3.1.1 Validation of Link Level.................... 13
3.1.2 Bit Error Rate Characteristic of NOMA . . . . . . . . . . . 16
3.2 Extension of the Vienna System Level Simulator . . . . . . . . . . 19
3.2.1 Details of the Vienna System Level Simulator . . . . . . . . 19
3.2.2 Extension for subband support................ 20
3.2.3 Extension for NOMA support ................ 23
3.2.4 Modelling Practical Receiver in System-level simulator . . . 24
3.2.5 BICM capacity......................... 25
3.2.6 Overview of the Vienna SL simulator with NOMA . . . . . 26
CHAPTER 4 SCHEDULER OF THE VIENNA SYSTEM-LEVEL SIMULATOR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
4.1 Estimate spectral efficiency...................... 28
4.1.1 MCSmap ........................... 28
4.1.2 Mapping function based on OMA .............. 29
4.1.3 BICMcapacity......................... 30
4.2 Improved method to construct MCS map . . . . . . . . . . . . . . 31
4.3 Scheduling Algorithm ......................... 32
4.3.1 Proportional Fair Scheduling for OMA . . . . . . . . . . . 32
4.3.2 Proportional Fair Scheduling for NOMA. . . . . . . . . . . 33
CHAPTER5 SCHEDULING PROBLEM .............. 35
5.1 Cross Entropy Method ........................ 35
5.2 Solving the scheduling problem.................... 37
5.3 Greedy Approaches to Scheduling .................. 39
5.3.1 Traditional Greedy Approach................. 39
5.3.2 Iterative Greedy Approach .................. 40
CHAPTER6 PERFORMANCE EVALUATION . . . . . . . . . . 41
6.1 Simulation Settings .......................... 41
6.2 Simulation Results........................... 41
6.2.1 Network scenario and comparison to OMA . . . . . . . . . 41
6.2.2 Performance of the proposed method . . . . . . . . . . . . 43
6.2.3 Resolution design of MCS map................ 45
CHAPTER 7 CONCLUSION AND FUTURE WORK . . . . . . 47
REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

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