第五代行動通信系統要求行動網路必須支援較高的同時服務的裝置數量、較高的網路傳輸速率、以及較高的頻譜效率。裝置對裝置(Device-to-Device, D2D)通訊技術也被認為是能夠達成第五代行動通信系統要求的重要技術之一。D2D通訊藉由重覆利用頻譜資源來增加頻譜效益和使用者數量，而重覆利用頻譜的D2D裝置會對其它使用者造成額外的頻道干擾，因此利用適當的頻譜資源分配和功率調配來減少頻道干擾、增加頻譜效益、提高系統吞吐量等是D2D通訊技術的研究重點。本篇論文的研究主題是如何在傳統無線網路下利用裝置對裝置傳輸技術來進行頻譜資源分配。
現今大多數的資源分配方法會使得基地台分配完所有的頻譜資源給D2D使用者。但是現實中的網路環境變化非常快速，使用者頻繁的移動模式會改變網路拓樸。如果基地台已經分配完所有的頻譜資源，一旦有新的使用者加入，基地台就必須重新分配資源和調配功率，所以多變的網路環境會大幅增加基地台的運算負擔。因此，為了讓基地台能夠更彈性的分配資原，本篇探討的議題為如何在滿足所有使用者的傳輸品質需求下，最小化被分配出去的頻譜資源數量。
我們的方法為兩階段的資源分享最佳化。最小化被分配出去的頻譜資源數量是一個NP-Hard的問題，無法再多項式時間內解出，因此我們在第一階段中利用condensation method將這個問題連續近似成一個凸優化問題(Convex optimization)來解出。第二階段則是在第一階段求出的資源分配結果下，為了最大化整體的傳輸速度，利用block successive upper-bound method進行傳輸功率調配。而實驗結果顯示我們的資源分配方法在使用的頻譜資源數量、傳輸速度上都有很好的表現。

Device-to-Device (D2D) communication is a promising solution to increase the system throughput and spectral efficiency in 5G mobile networks. However, D2D communication may cause serious mutual interference among D2D pairs and cellular users (CUEs) because D2D pairs and CUEs share the same radio resource. In this thesis, we formulate the uplink resource sharing problem and propose the Two-Stage Resource Sharing Optimization (TCBO) algorithm. In order to make base station allocate resource blocks (RB) more flexibly, the primary goal of the proposed algorithm is to minimize the number of resource blocks reused by D2D pairs while satisfying SINR requirements of all user. The secondary goal is to maximize the overall system throughput. Two-stage resource sharing optimization algorithm we propose contains two parts. The primary goal of the resource allocation problem is a NP hard problem, so the first-stage of our algorithm uses the condensation method to formulate the primary goal as a geometric programming (GP) and then solve it with convex optimization methods. Given the results of the first stage, the second-stage of our algorithm exploits the block successive upper-bound method (BSUM) to formulate the secondary goal as a convex optimization problem. Simulation results show that the two-stage resource sharing optimization algorithm not only reduces the number of reused RBs, but also results in high system throughput.

Abstract 3
中文摘要 4
Table of contents 5
List of Figures 7
Chapter 1 8
Introduction 8
Chapter 2 10
Related work 10
Chapter 3 13
System model 13
3.1 System model 13
3.2 Problem formulation 15
Chapter 4 19
First-stage resource allocation 19
4.1 Geometric programming 19
4.2 Approximate a non-convex optimization problem to a geometric programming 20
4.3 Accelerate the geometric programming 23
4.3 Results of the first-stage resource allocation 24
4.4 First-stage resource allocation algorithm 25
Chapter 5 27
Second-stage power control 27
5.1 BSUM in two users case 29
5.2 BSUM in multiple user case 34
5.3 Second-stage power control algorithm 35
Chapter 6 37
Simulation 37
6.1 Compared algorithms 37
6.1.1 Optimal resource allocation algorithm 37
6.1.2 Greedy resource allocation algorithm 38
6.1.3 Greedy throughput maximization algorithm 38
6.1.4 Maximum independent set based and Stackelberg power based algorithm 38
6.2 Simulation settings 39
6.3 Simulation results 40
Chapter 7 45
Conclusion 45
Reference 46

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