近年來越來越多的通訊裝置被大量製造出來，並且裝置所需要的服務品質 (Quality of service)也越來越高，在頻寬有限的情況下，如何有效的利用頻寬來服務更多通訊裝置以及提升總體傳輸量是一個重要議題，而在本篇論文中研究的主題是在傳統無線網路下裝置對裝置(Device-to-device)技術。
在傳統無線網路下裝置對裝置技術裡，由於只要兩個裝置間的距離夠小就能夠直接互相溝通(Device-to-device communication)，不需要在透過基地台(Base station)幫忙轉傳，降低了基地台的負擔，以及能改善整體的傳輸量。然而，能夠進行D2D communication的裝置與傳統裝置是共享同一個頻段， 於是當越來越多的D2D裝置使用同一個頻段時，彼此間的干擾也會越來越大，所以如何有效快速的對D2D裝置作頻段(資源)分配來降低干擾使得整體傳輸量上升以及讓更多的D2D裝置能共同使用頻段，便是我們此篇探討的重點議題。
我們的環境設定裡允許一個傳統裝置所擁有的一個資源能夠被多個D2D裝置所共享，而每個D2D裝置只能使用一個資源，以及每個傳統裝置與D2D裝置都有服務品質的需求需要被滿足，盡可能使越多裝置與整題傳輸量最大化，在這些限制下，原始問題是一個在非多項式(NP)時間內可解的難題，於是我們利用分類的方式與一個最大獨立集(Maximum independent set)近似法(Heuristic)來加速找出彼此干擾較小的D2D裝置們來使用同個資源並且利用電力控制來讓更多D2D裝置有機會能共享。除此之外，我們也提出一個機制來解決當傳統裝置的數量多於資源的數量時，我們該如何挑出適合的傳統裝置來使用這些資源。
最後，實驗結果顯示出我們的資源分配演算法在執行時間、D2D裝置使用率與整體傳輸量效能指標上都有很好的表現。

Device-to-device (D2D) communication underlying cellular networks is a promising solution to increase system throughput and to achieve higher spectral efficiency. However, D2D communications may cause serious mutual interference among D2D pairs and cellular users (CUEs) due to D2D pairs and CUEs share the same radio resource. In this paper, we formulate the uplink resource allocation problem and propose a fast resource allocation algorithm called Maximum independent set based and Stackelberg power based (MiSo) algorithm to get good system throughput. In the MiSo algorithm, we exploit both maximum independent set and power control to allow more D2D pairs able to reuse the same resource block, thus improving system throughput. Furthermore, we propose a mechanism that allocates resource blocks (RB) to proper CUEs in the case when the number of CUEs is larger than the number of RBs. Simulation results show that the MiSo algorithm not only is superior in running time, but also has the splendid percentage of permitted DUE pairs and system throughput.

Abstract iii
中文摘要 iv
Table of contents v
List of figures vii
Chapter 1 Introduction 1
Chapter 2 Related work 3
Chapter 3 System model 6
3.1 System model 6
3.2 Problem formulation 8
Chapter 4 Resource allocation algorithm 11
4.1 Determining reasonable initial power for D2D pairs 11
4.2 Selecting proper CUEs mechanism 12
4.3 Maximum independent set based and Stackelberg power based algorithm 16
4.4 Power control based on Stackelberg game 21
Chapter 5 Simulation 24
5.1 Compared algorithm 24
5.1.1 Stackelberg game based power control and resource allocation algorithm 24
5.1.2 Greedy throughput maximization algorithm 25
5.1.3 Greedy resource allocation algorithm 25
5.1.4 Optimal resource allocation algorithm 26
5.2 Simulation settings 26
5.3 Simulation results 27
Chapter 6 Conclusion 32
Reference 33

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