指導教授：魏宏宇臺灣大學：電機工程學研究所廖冠傑Liao, Kuan-ChiehKuan-ChiehLiao2014-11-282018-07-062014-11-282018-07-062014http://ntur.lib.ntu.edu.tw//handle/246246/262920Device-to-Device (D2D) communications provides a proximity service, consuming less energy and having higher spectrum reuse. It has become more and more popular in recent years. In our work, we consider that the devices in a cell request the same data from a base station (BS). The devices will form some clusters to receive data. Every cluster will have one device be central entity. The central entity in a cluster receives the data from the BS, and then broadcasts the data to all other devices in the same cluster. The central entity suffers from the cost of transmit power consumption, which discourages the devices from being the central entity. As the devices are selfish in maximizing their own utility, game theory serve as a powerful technique for analyzing the behavior of the devices. We formulate the selfish and non-cooperative interaction of the devices under the system as a game problem. To solve this problem, we propose a central-entity-election mechanism that motivates the devices to report the true transmission costs, and elects the most appropriate devices as the central entities to reach the maximum system utility (social welfare). On the other way, we prove that the multiple-cluster central entity election is a NP hard problem. To avoid the NP hard problem, we propose the distributed central entity election learning (DCEE) algorithm to form clusters. We prove the DCEE algorithm can always converge and have many desirable properties as budget balance and individual rationality. In the simulation part, we verify the theoretical analysis in a real LTE system setting. With the proposed mechanism and the simulation results, D2D communications is shown to have the potential to improve the performance of wireless networks.Contents 口試委員會審定書 摘要ii Abstract iv 1 Introduction 1 2 Related Work 4 3 D2D System Framework 7 3.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 3.2 User’s Utility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 4 Central-Entity-Election Mechanism For One Cluster System 10 5 Auction Game in Mechanism 13 6 Analysis – the Equilibrium and the Desirable Properties 15 7 Extension From One-Cluster to Multiple-Cluster System 20 7.0.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 7.1 Centralized System Analysis . . . . . . . . . . . . . . . . . . . . . . . . 20 vi 7.2 Distributed System Analysis . . . . . . . . . . . . . . . . . . . . . . . . 22 7.2.1 User’s Utility . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 8 Distributed Central Entity Election(DCEE) Algorithm 26 8.1 Distributed Central Entity Election(DCEE) Algorithm . . . . . . . . . . . 26 8.2 The Convergence of the DCEE Algorithm . . . . . . . . . . . . . . . . . 28 9 Properties and Theorems of the DCEE Algorithm 33 10 Further Investigation of the DCEE Algorithm and Discussion 36 10.1 Theoretical Analysis in Small Step Size b . . . . . . . . . . . . . . . . . 36 10.2 Discussion and Comparison to Related Work . . . . . . . . . . . . . . . 38 11 Simulation Results 39 11.1 Simulation Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 11.2 Verification of the Theoretical Analysis in the Auction Mechanism Design 40 11.2.1 Truth Telling . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 11.2.2 Maximum Cluster Utility . . . . . . . . . . . . . . . . . . . . . . 41 11.2.3 Effect of the charge parameter . . . . . . . . . . . . . . . . . . 42 11.3 Verification of the Theoretical Analysis in DCEE algorithm . . . . . . . . 43 11.4 Observation in Different Parameters . . . . . . . . . . . . . . . . . . . . 43 11.4.1 Change Step Size b . . . . . . . . . . . . . . . . . . . . . . . . . 44 11.4.2 Change Transfer Price T . . . . . . . . . . . . . . . . . . . . . . 45 11.4.3 Change Initial condition pi(0) . . . . . . . . . . . . . . . . . . . 46 11.5 Oscillation Phenomenon . . . . . . . . . . . . . . . . . . . . . . . . . . 47 11.6 Compare Social Welfare . . . . . . . . . . . . . . . . . . . . . . . . . . 48 vii 12 Conclusion 50 Bibliography 51817696 bytesapplication/pdf論文公開時間：2017/08/21論文使用權限：同意無償授權裝置對裝置集團中央節點賽局理論機制設計學習演算法thesishttp://ntur.lib.ntu.edu.tw/bitstream/246246/262920/1/ntu-103-R01921036-1.pdf