無線傳輸環境，在有限頻譜資源下，用戶需求持續增長，頻譜重用技術包
含非正交多重存取(non-orthogonal multiple access) 與波束多重存取技術(beam
division multiple access) 的使用越來越廣泛。我們考慮下行傳輸的單一細胞
內，基站傳輸具備非正交多重存取與波束多重存取功能。給定每個頻譜資源的
功率額度，我們的首要目標為滿足最多用戶們的傳輸速率需求，滿足條件為大
於或等於最低速率。第二目標使用最少數量的頻譜資源，來達成前一個目標。
第三目標為在滿足前兩目標的條件下，最大化系統的資料傳輸量。
本篇論文，我們提出了兩種算法。第一個是最高效率優先（HEF）算法。
第二種是匈牙利三分法（HT）算法。
我們提出的HEF演算法分為三個部分：群組化，決定組數規模和頻譜資源分
配。群組化部分是在給定組數規模的前提下，滿足最多的用戶需求；每個扇形
區，根據各用戶的效率因子來進行群組化。決定組數規模部分，此部分通過二
分逼近法，遞歸式執行群組化，以找出能滿足相同用戶需求所需之最少頻譜資
源數量。頻譜資源分配部分，目的為降低不同扇形區進行頻譜重用所產生的同
頻干擾，分配策略是讓相鄰扇形區避免重用相同頻譜資源。
匈牙利三分法（HT）算法，通過兩次經典匈牙利算法，將一組內的人數提
升到三人，效益最高的用戶優先得到頻譜資源，以獲得最大化系統傳輸量的總
組數。其餘部分與HEF 相同。
實驗結果表明，我們提出的方法可以在略微增加計算時間的情況下顯著增加
滿足需求用戶的數量。而且我們的方法在功耗和頻譜效率方面的表現優於現有
方法。

We consider a mobile network in which base stations support non-orthogonal multiple access (NOMA) and beam division multiple access (BDMA) in the downlink direction. Given the power budget per resource block (RB), our primary goal is serving as many users as possible at rates equal to or beyond their minimum rate requirements; the secondary goal is minimizing the number of RBs required to achieve the primary goal; and the third goal is maximizing system throughput under the condition that the primary and secondary goals is achieved.

In this thesis, there are two algorithms we proposed. The first one is the highest-efficiency-first (HEF) algorithm. The second one is the hungarian tripartite (HT) algorithm.

To improve spectral efficiency, we develop the highest-efficiency-first (HEF) algorithm, which could be divided into three processes —grouping, group scale determination, and RB allocation. Given the group scale (which is defined as the total number of groups in a sector), the grouping process aims to satisfy the user’s minimum rate requirements of celluar user equipments (CUEs) as many as possible. In each sector, groups are formed according to CUEs’ efficiency.

The group scale determination process tries to satisfy the user’s demand rate of as many CUEs as possible by utilizing the fewest RBs. This process finds out the optimal number of RBs by the bisection method that recursively runs the grouping process.

The RB allocation process aims to minimize the interference caused by reuse of the same RBs in multiple sectors. The RB allocation strategy is applied by avoiding reusing the same RB of the adjacent sectors as possible as we can.

Hungarian tripartite (HT) algorithm is a method based on classic hungarian algorithm. We extend the classic method to 3 users grouping by conducting classic hungarian algorithm twice. The same concept of user’s efficiency is also applied in HT. By this, we can find all groups with the maximum system throughput. After considering the total number of NOMA groups and RBs, we conduct the RB allocation in the same way as HEF.

Experimental results show that our proposed methods can significantly increase the number of well-served CUEs under slightly increasing computation time. And our methods’ performances are better than existing methods in power consumption and spectral efficiency.

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
中文摘要 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . vii
CH.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 NOMA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 Successive Interference Cancellation (SIC) . . . . . . . . . . . 4
1.3 Beamforming and Beam Division Multiple Access (BDMA) . . . . . . 5
CH.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . 8
CH.3 System Model . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.1 BDMA Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3.2 NOMA with BDMA Scheme . . . . . . . . . . . . . . . . . . . . . 15
3.3 Objectives and Constraints . . . . . . . . . . . . . . . . . . 17
CH.4 Proposed Method: HEF . . . . . . . . . . . . . . . . . . . . . 19
4.1 Overview of HEF . . . . . . . . . . . . . . . . . . . . . . . . 19
4.2 Grouping of HEF . . . . . . . . . . . . . . . . . . . . . . . . 22
4.2.1 OMA Scheme . . . . . . . . . . . . . . . . . . . . . . . . . 23
4.2.2 2-NOMA Scheme . . . . . . . . . . . . . . . . . . . . . . . . 26
4.2.3 3-NOMA Scheme . . . . . . . . . . . . . . . . . . . . . . . . 29
4.2.4 Final Power Allocation . . . . . . . . . . . . . . . . . . . 31
4.3 Group Scale Determination using The Bisection Method . . . . . 33
4.4 RB Allocation of HEF . . . . . . . . . . . . . . . . . . . . . 36
CH.5 Proposed Method: HT . . . . . . . . . . . . . . . . . . . . . 39
CH.6 Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . 43
6.1 Compared Algorithms . . . . . . . . . . . . . . . . . . . . . . 43
6.1.1 Optimal OMA Resource Allocation Algorithm . . . . . . . . . . 44
6.1.2 Channel State Sorting Pairing Algorithm . . . . . . . . . . . 45
6.1.3 Maximum Weight Matching Algorithm . . . . . . . . . . . . . . 46
6.2 Simulation Settings . . . . . . . . . . . . . . . . . . . . . . 48
6.3 Simulation Results . . . . . . . . . . . . .. . . . . . . . . . 51
6.4 Simulation of The Special Case . . . . . . . . . . . . . . . . 59
CH.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . 65
Reference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

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