在設備到設備(Device-to-Device, D2D)通訊中，在小區域內行動設備不用通過基地台(evolved Node B, eNB)來進行路由及傳輸。在本論文中，我們考慮D2D兩個主要方面的研究。首先我們考慮在D2D群播環境下進行一次轉傳的情境，D2D使用者(DUs)與蜂巢網路使用者(CUs)具有不同需求的傳輸速率及信道質量指標（Channel Quality Indicator, CQI）。我們提出以下三個問題:
第一個問題是最大化符合用戶滿意的吞吐量。我們提出了一個近似演算法來解決這個問題，近似比為2 max(N_C,N_D). 在此N_C及N_D分別為CU和DU的數量。第二個問題我們假設當用戶需求的傳輸速率被滿足時，會提供基地台報酬。因此，我們的目標是最大化基地台的收入。首先我們證明此問題是NP-hardness的問題，並提出了一個啟發式和兩個近似演算法，近似演算法的近似比分別為2和2g，其中g是我們在群播中利用CQI來分組的組數量。我們另外針對可以滿足最群使用者數量的問題，提出一個啟發式演算法。在第三個問題，我們將問題轉成一個基於預算最大化覆蓋的問題，並增加須滿足使用者需求的限制。針對此問題，我們提出兩種近似演算法來最大化基地台的收益，近似比分別為1-1/√e和1-1/e ，其中e表示自然對數的基數。
在本論文的第二部分，我們研究基於激勵機制的D2D轉傳問題。內容供應商尋求基地台在網路中轉為傳播訊息。基地台利用提供報酬的機制來激勵DU轉傳訊息。當訊息到達訂閱用戶時，內容供應商會提共報酬給基地台。我們目標是將基地台從內容供應商得到的報酬最大化。然而，基地台的報酬不能超過每筆訊息供應商設定的預算，並且轉傳的DU也會有傳輸能力的限制。我們提出兩個演算法解決以上的問題。第一個演算法利用動態規劃將存在DU的訊息，傳輸給鄰近的DUs，並考慮DU傳輸能力和每筆訊息預算的限制。第二種演算法基於最大流量與線性鬆弛技術解決此問題。實驗結果顯示，我們所提的演算法的效能優於其它方法。

In Device-to-Device (D2D) Communication, mobile devices in a small geographical region communicate directly without the need of routing the traffic through base station (eNB). In this dissertation, we consider two main aspects of D2D research.
First, we consider a two-hop underlay D2D multicast scenario, where cellular users (CUs) and D2D users (DUs) have different data request rates and channel quality indicator (CQI) values. We address three problems. Our first problem is to maximize the user’s satisfied throughput. We propose an approximation algorithm to solve this problem, with an approximation ratio of 2 max(N_C,N_D). Here N_C and N_D is the number of CUs and DUs respectively. In the second problem, we assume that when a user’s data request is satisfied, it offers a unique revenue to the eNB. Here, our objective is to maximize the revenue of the eNB. We prove its NP-hardness and propose a heuristic and two approximation algorithms that have approximation ratio of 2 and 2g respectively, where g is the number of CQI groups we use in multicast. Additionally, we aim to maximize the number of satisfied users and propose a heuristic algorithm. In the third problem, we model our problem as a budgeted maximum coverage problem by adding a constraint to the user’s satisfying request. We propose two approximation algorithms to maximize the revenue of eNB. These algorithms have approximation ratio of 1-1/√e and 1-1/e respectively, where e denotes the base of natural logarithm.
In the second part of this dissertation, we investigate a problem on incentive-based D2D relaying. Here a content provider seeks eNB’s assistance to disseminate the message in the network. The eNB incentivize the DUs to relay the message. When the message reaches the subscribed user, the content provider pays revenue to eNB. Our objective is to maximize the revenue of eNB collected from a content provider. However, eNB's revenue cannot exceed the message budget limit, and transmission budget limits each DU’s relaying. We propose two algorithms. The first algorithm uses the dynamic programming approach where a DU choose the suitable messages to send to the neighbor by considering the transmission budget and message budget. The second algorithm uses the maximum flow with the relaxation and rounding techniques. The experimental results show that the proposed algorithms perform well regarding collected profit while comparing to the candidate algorithms.

中文摘要 i
Abstract ii
Acknowledgements iv
Contents v
List of Figures viii
List of Tables ix
Chapter 1 1
Introduction 1
1.1 Background to Device-to-Device Communication 1
1.2 Motivation 3
1.3 Contributions 6
1.4 Dissertation Organization 8
Chapter 2 9
Related Works 9
2.1 Resource Allocation Schemes in D2D Multicast 9
2.2 Incentive-Based D2D Caching and Relaying 12
Chapter 3 14
Resource Allocation Algorithms for Profit Maximization in D2D Multicast 14
3.1 Problem Scenario 14
3.2 Maximizing the Satisfied User’s Throughput 15
3.2.1 System Model 15
3.2.2 Algorithm to Maximize the Satisfied Throughput (ST) 17
3.2.3 Algorithm to Maximize the Number of Satisfied User (SU) 21
3.3 Maximizing the Satisfied User’s Profit 24
3.3.1 System Model 24
3.3.2Maximizing the Satisfied User’s Profit and Satisfied User Count 26
3.3.3Maximizing the Satisfied Profit: NP-Hardness proof 27
3.3.4 Maximizing the Satisfied Profit: Greedy Heuristic 31
3.3.5 Maximizing the Satisfied Profit: First Approximation Algorithm 37
3.3.6 Maximizing the Satisfied Profit: Second Approximation Algorithm 39
3.3.7 Maximizing the Satisfied User Count: Greedy Heuristic 40
3.4 Maximizing the Satisfied User’s Profit with a Constraint 41
3.4.1 Problem Formulation 42
3.4.2 NP-Hardness Proof 45
3.4.3 Problem Modelling 47
3.4.4 Algorithms for Satisfied User’s Profit Maximization 48
3.5 Simulation Results 53
3.5.1 Maximizing the Satisfied User’s Throughput 53
3.5.2 Maximizing the Satisfied User’s Profit 57
3.5.3 Maximizing the Satisfied User’s Profit with a Constraint 64
Chapter 4 70
Incentive-Based Relaying in D2D Networks 70
4.1 Problem Scenario 70
4.2 Problem Formulation of Incentive-Based Relaying 70
4.2.1 eNB’s Revenue Maximization Equation 74
4.2.2 Algorithms 76
4.2.3 Algorithm 1: Dual Dynamic Programming (DDP). 77
4.2.4 Algorithm 2: Maximum Flow-based Relaxation (MFR). 80
4.3 Simulation Results 84
4.3.1 Incentive-Based D2D Relaying 84
Chapter 5 89
Conclusion and Future work 89
Appendix 91
References 95

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