基於5G多連接的毫米波無縫的虛擬實境串流媒體服務換手預測

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姓名

劉肇中(JHAO-JHONG LIU) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

基於5G多連接的毫米波無縫的虛擬實境串流媒體服務換手預測
(Predictive Handover for MmWave Seamless VR Streaming Services Based on 5G Multiconnectivity)

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至系統瀏覽論文 ( 永不開放)

摘要(中)

技術知識不斷引導我們進入一個智能城市環境這使我們周圍有大量的通信和計算，並且隨著5G時代的到來，新的體驗和服務會給我們帶來新的視角。首先我們先從5G與邊緣運算技術舉證目前可以達到幾乎0段訊，由此可見用戶應該在日常路線上獲得順暢的服務，其中我們觀察到大多數人通常會經過同一條路線並結合谷歌地圖和MEC架構等應用，我們可以通過將每條路線視為一個景況模型，讓設備/車輛提前知道上環境信息。因此我們設計QoS drop的事件的預測用以預測即將造成訊號品質的事件，由此可以設計提前避開或服務轉移的方法。從結果可以看出我們的方法與效能皆為最佳，我們繼續研究基地排的選擇與MEC的最佳化的重要基石。

摘要(英)

Techknowledge keep leading us into a smart city environment which massive communication and computing around us. besides, with the advent of the 5G era, new experiences and services will bring us a new perspective. User should have a smooth service on everyday route while most of people usually passing the same route. Along with application like google map and MEC architecture, we could let device/ vehicle know the context information in advanced by regarded every route as a context model. We propose a new prediction for future service(e.g. VR streaming), which can predicting every time there will be a service drop. Not only integrates signal processing capabilities but also considers future high mobility. In order to proactive to every Qos drop letting users are available to experience VR streaming services while everyday driving.

關鍵字(中)

★ 5G
★ 毫米波
★ 換手預測
★ 移動性管理
★ 服務轉移

關鍵字(英)

★ 5G
★ millimeter wave
★ handover prediction
★ mobility
★ service migration

論文目次

Table of Contents
摘要 I
Abstract II
致謝 III
Table of Contents IV
List of Figures VI
List of Tables VIII
1. Introduction 1
1.1 Introduction 1
1.2 Future Service Requirement and Challenge 4
1.3 QoS Drop Prediction 6
1.4 Why machine learning 9
2. Background 11
2.1 Device-Centric Coordinative Network 11
2.2 Service Migration 12
2.3 Handover prediction integration with service migration 13
3. Related Works 14
3.1 Qos-drop event Prediction 14
3.2 Handover Decision 15
4. Preliminary 17
4.1 RNN-Based 17
4.2 DNN-Based 19
5. SYSTEM MODEL 21
5.1 System and Architecture 22
5.2 Opportunity and Solution 23
5.3 System Architecture 23
5.4 Dataset and Scenario 24
5.5 Reshape and Filter 34
5.6 Architecture and Model 36

6. SIMULATION AND EVALUATION 39
7. CONCLUSION AND FUTURE WORK 45
Reference 46

參考文獻

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指導教授

吳曉光

審核日期

2021-9-2

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