隨著手持行動裝置數量的快速增加，因此在有限的頻段內，未來如何能提供大量的資料傳輸是一個很重要的問題。在現在的標準下，像是Long-Term Evolution (LTE)，是以正交多工(OMA) 技術為基礎做設計的。然而，這並沒有充分地利用頻譜資源。因此，非正交分頻多工(NOMA) 這項技術就被提出，用來改善資料傳輸量。不同於正交分頻多工，非正交分頻多工系統中，每個頻譜資源可以分配給多個使用者，達到較佳的頻譜使用效率。但是，同個頻譜內的使用者彼此之間會造成干擾，使得偵測訊號困難。在現在大部分的非正交分頻多工研究中，大部分都是下行系統，使用者只能使用Successive Interference Cancellation(SIC) 做為偵測訊號的方法，因為使用者通常只有單天線，使得偵測訊號效果不佳。在本篇論文中，我們著重在上行非正交分頻多工系統的部分。我們提出了頻段分配的演算法，將這些頻段分給使用者以達到最佳的使用效率，並且在固定使用者的數量(K)下，試著去找到L(每個頻段可以分給多少使用者) 和Ns(每個使用者可以使用多少頻段)的組合能達到最佳的資料傳輸量。在基地台端，因為頻段與頻段彼此之間不會互相影響，所以我們可以分別對每個頻段做偵測訊號的動作。並且可以利用多天線的好處，使用不同多天線偵測訊號的方法去做處理。在錯誤率上的表現比下行系統還要來的好。

With the rapid growth of number of mobile devices, to achieve the huge throughputs becomes an important issue in the future. In the current mobile communication systems (such as Long-Term Evolution(LTE)), Orthogonal Multiple Access (OMA) techniques has been adopted. However, it does not fully utilize the spectrum resource. Therefore, Non-Orthogonal Multiple Access (NOMA) was proposed to enhance the performance of throughputs.
Different from OMA system, each subband can be allocated to more than one users in NOMA system, which achieves better spectrum efficiency. However, the users cause interference to other users in the same subband, making detection process more complex. In the current NOMA researches, it mainly focus on downlink system, the Successive Interference Cancellation (SIC) was used for detection. However, the performance of Bit Error Rate
(BER) is not good enough. In this thesis, we focus on uplink NOMA system. We propose an algorithm to allocate the subbands to users, and we try to find the best combination of L(number of users on each subband) and Ns(number of subbands that each user can use) to
achieve the maximum sum rates for fixed K(the number of users). At the base station, we perform detection subband by subband, since subbands will not cause interference to each other. In uplink system, multiple antennas can be used for detection. Several low complexity multiple input multiple output (MIMO) detection algorithms are applied. The performance of BER will be better than downlink system.

Chinese Abstract i
English Abstract ii
Contents iii
1 Introduction 1
2 Background 5
2.1 OMA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1.1 FDMA Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1.2 OFDM Systems [1] . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.1.3 OFDMA System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.2 Prior Arts of NOMA and SCMA Systems . . . . . . . . . . . . . . . . . . . 17
2.2.1 NOMA [7] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.2.2 SCMA [13] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3 Proposed Uplink NOMA Scheme 21
3.1 System Descriptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.2 Subband and Power Allocation for NOMA Systems . . . . . . . . . . . . . . 24
3.2.1 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
3.2.2 Proposed Subband and Power Allocation Algorithm . . . . . . . . . . 28
3.3 Subband Assignments for Every Users . . . . . . . . . . . . . . . . . . . . . 32
3.4 MIMO Detection for NOMA Systems . . . . . . . . . . . . . . . . . . . . . . 34
3.4.1 Problem Re-formulation . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.4.2 MIMO Detection Algorithms [2] . . . . . . . . . . . . . . . . . . . . . 35
4 Simulations 38
4.1 The Comparison of Average Rates Per User for Fixed K . . . . . . . . . . . 38
4.2 The Comparison of Bit Error Rate (BER) with Different Number of L . . . 42
4.3 The Comparison of Bit Error Rate (BER) with Different MIMO Detection
Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
4.4 The Comparison of Bit Error Rate (BER) with Different Number of Receive
Antennas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
4.5 The Comparison on Overall Throughputs for Different Number Subbands
Allocated to Each User . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
4.6 The Comparison for Average Rates Per User for the Above Three Cases . . . 48
5 Conclusions 50
Bibliography 51

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