臺灣博碩士論文加值系統

由於「非正交多重存取」傳輸系統具有相對較大的區域覆蓋範圍，且相對於傳統正交多重存取系統，也可達到更佳的用戶訊息吞吐量，因此非正交多重存取傳輸系統近年來為研究者所廣泛討論。在非正交多重接取傳輸系統中，用戶端(User Equipments)以及基地台(Base Station)需具備優越的干擾消除能力以消除從其他用戶來的信號干擾。在此論文中，我們將以上行多重接取傳輸系統搭配星座圖能量分配(Constellation Power Allocation)的傳輸架構來分析系統效能。在通道模型的部分，我們考慮具有隨機相位偏移的高斯多重存取通道。不同於引用論文所使用的抑制星座圖總合通道容量之目標函數，我們提出多個以歐式距離為基礎之目標函數，用於決定在具有隨機相位偏移的兩用戶高斯多重存取通道之星座圖能量分配的能量分配參數。在論文中，我們亦以範例討論如何延伸到三個用戶的系統，並提出具可行性的能量分配法則。

Recently, non-orthogonal multiple access (NOMA) technology has been widely dis-
cussed due to its better cell coverage and potentially higher user throughput than the
traditional orthogonal multiple access (OMA) technology. In its implementation, user
equipments (UEs) and the base station (BS) of NOMA are required to have an advanced
interference cancellation capability in order to eliminate the signals generated by other
users. In this thesis, we focus on the Constellation Power Allocation (CPA) transmission
scheme and investigate its system performance over the uplink NOMA scenario. The
channel model considered is the Gaussian Multiple Access Channel (GMAC) with addi-
tional random phase offset. Several alternative distance-based objective functions, other
than the usual CC sum capacity objective function in the literature, for the algorithmic
determination of the power allocation factor of the CPA scheme over a two-user GMAC
with random phase offsets are proposed. Possible extension to the three-user GMAC with
random phase offset is also addressed.
i

Chinese Abstract i
Abstract ii
Acknowledgement iii
Contents iv
List of Figures vi
List of Tables xi
1 Introduction 1
2 System Model and Background 3
2.1 Application Scenarios for Non-Orthogonal Multiple Access (NOMA) . . . . 3
2.2 Uplink Non-Orthogonal Multiple Access (NOMA) . . . . . . . . . . . . . . 6
2.3 Multilevel Coding for NOMA . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.3.1 Multilevel Code Encoder . . . . . . . . . . . . . . . . . . . . . . . . 8
2.3.2 Multistage Decoder for Multilevel Codes . . . . . . . . . . . . . . . 8
2.4 Joint Maximum-Likelihood (JML) Receiver . . . . . . . . . . . . . . . . . . 9
2.5 Successive Interference Cancellation (SIC) Receiver . . . . . . . . . . . . . 11
3 Previous Work and Problem Formulation 12
3.1 Constellation Power Allocation (CPA) Scheme . . . . . . . . . . . . . . . . 13
3.2 Capacity Region and Constellation Constrained (CC) Sum Capacity for
GMAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3.2.1 Capacity Region for GMAC . . . . . . . . . . . . . . . . . . . . . . 14
3.2.2 Constellation Constrained (CC) Sum Capacity for GMAC . . . . . 15
3.2.3 Constellation Constrained (CC) Sum Capacity for GMAC with
Random Phase Effect . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.3 Power Allocation Based on CC Sum Capacity . . . . . . . . . . . . . . . . 17
3.4 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
4 Criterions for Power Allocation 19
4.1 Motivations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
4.2 Suboptimal Power Allocation for Three-User GMAC with Random Phase
Offset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
4.3 System Setting for Simulations . . . . . . . . . . . . . . . . . . . . . . . . . 21
5 Simulation Results 24
5.1 Two-user GMAC with Equal Average Power Constraint and Random Phase
Offset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
5.2 Two-user GMAC with Unequal Average Power Constraint and Random
Phase Offset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
5.3 Three-user GMAC with Equal Average Power Constraint and Random
Phase Offset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
6 Conclusion and Future Work 85
Bibliography 87

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