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研究生:謝政佑
研究生(外文):Cheng-YuHsieh
論文名稱:結合社群網路輔助邀請之群播廣播服務多重費率計費方法
論文名稱(外文):Multi-tariff Charging for Multicast and Broadcast Services with Social-assisted Invitation
指導教授:蘇淑茵蘇淑茵引用關係
指導教授(外文):Sok-Ian Sou
學位類別:碩士
校院名稱:國立成功大學
系所名稱:電腦與通信工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:英文
論文頁數:52
中文關鍵詞:群播廣播服務小世界網路線上社群網路社群影響
外文關鍵詞:MBSsmall-world networkonline social networksocial influence
相關次數:
  • 被引用被引用:0
  • 點閱點閱:100
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  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
次世代群播廣播服務可以經由共同連線傳向多重目的地的方式減少網路成本耗費。當一群線上使用者收到相同的群播廣播服務內容時,每個線上使用者的平均網路耗費成本可以大量減少。近來由於線上社群網路的興起,使得資訊可以經由大量人際間的聯絡溝通而傳遞出去。本篇論文主要在研究如何經由人與人之間的感染力來影響每個人對於使用群播廣播服務的態度與看法。我們提出了一個社群網路輔助邀請機制來模擬朋友間對於群播廣播服務內容看法的形成。在文章最後,我們以數據上的結果來說明我們的社群網路輔助邀請機制可以應用在分析群播廣播服務的多重費率計費方法上。
The next generation MBS could reduce the network cost through common access links towards multiple destinations. When a group of online users receive the same MBS content, the average MBS network cost per user can be significantly reduced. Recently, Online Social Network (OSN) provides new means of disseminating information through group of people via interpersonal communications. This thesis studies how the interpersonal influence that affects one's attitudes and opinions in MBS. We propose a social-assisted scheme to formulate the opinion on MBS content with friends. Numerical results demonstrate that the trend prediction from the social-assisted scheme about MBS user arrival can be used to analyze multi-tariff charging method for MBS.
Abstract I
摘要 II
誌謝 III
List of Tables V
List of Figures VI
Chapter 1 Introduction 1
Chapter 2 Related Works 6
Chapter 3 Background 9
3.1. Watts-Strogaz Model 10
3.2. Social Influence Network Theory 12
3.3. Zipf’s Law 14
3.4. Jaccard Similarity Coefficient 14
Chapter 4 Our Proposed Social-assisted Model 15
4.1. Solution Framework 16
4.2. The Expected Size of the Subscriber’s Interests Set 19
4.3. The Expected Size of the Intersection of Two Subscribers’ Interests Sets 20
4.4. The Expected Size of the union of Any Two Subscribers’ Interests Sets 21
4.5. The Expected Value of the Jaccard similarity coefficient 23
4.6. The Expected Influence Probability 25
4.7. The Expected Influence Rate 26
Chapter 5 Numerical Examples 27
5.1. Verification of the Social-assisted Model and the Simulation Results 29
5.2. Effects of the Reconnect Probability in WS Model 33
5.3. Effects of the Susceptibility of Subscribers 36
5.4. Effects of the Active Proportion 39
5.5. Effect on Operator’s Revenue 43
Chapter 6 Discussion 45
Chapter 7 Conclusions and Future Works 49
References 50


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