運用強化學習與模糊理論於叢集式車載網路及其異質網路存取方法之研究

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姓名

陳昱伶(Yu-Ling Chen) 查詢紙本館藏

畢業系所

通訊工程學系

論文名稱

運用強化學習與模糊理論於叢集式車載網路及其異質網路存取方法之研究
(Study of Cluster-based Vehicular Network for Heterogeneous Network Access by Using Reinforcement Learning and Fuzzy Theory)

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摘要(中)

汽車移動物聯網技術(簡稱：車聯網)是因應自動駕駛而發展的重要技術，目前第三代合作夥伴計劃(the Third Generation Partnership Project，3GPP)與Wi-Fi聯盟(Wi-Fi Alliance，WFA)兩大通訊標準組織正積極研究相關技術。由於兩組織所訂定的標準擁有各自的特色，再加上車輛高速移動與拓撲快速變化的特性，在各自傳輸資源有限的狀態下，車輛該如何抉擇通訊界面將是未來需要解決的問題。
然而將車輛分散於兩種通訊界面中，傳輸資源也會因為每一輛車與中心設備(AP、Evolved Node B(eNodeB)…等)建立連線所需的控制訊息而佔據多數資源，因此該如何設計資料傳輸路徑，使得有些車輛可以不用與中心設備建立連線，而是透過鄰近車輛幫忙轉送，以降低車輛與中心設備所建立的連線數將是重要議題之一。
本論文所設計的演算法根據系統效能與車輛所給予的資訊，判斷車輛該選擇何種通訊介面，以及如何將車輛分群，以提高資料傳送成功率與有效降低連線數。

摘要(英)

Vehicular communication is a technology developed to support autonomous driving. Recently, the two major communication standards organization, Third Generation Partnership Project (3GPP) and Wi-Fi Alliance (WFA), are actively studying related technologies. However, each of the two standards has its own characteristics, along with some features such as high mobility of vehicles, frequent topology changes and limited transmission resources. Consequently, how to select the communication interface will be the problem that we should solve in the future.
However, even if we disperse the vehicles into two different communication interface, the control signaling overhead caused by the connections between vehicles and the central equipment will exhaust the transmission resources. Therefore, the issue of designing a proper transmission model will be important. In other words, we aim to reduce the number of connections between the vehicle and the central equipment, achieved by forcing the vehicle to transmit data via its neighbor.
According to information given by vehicles and environment condition of the system, we proposed an algorithm that decides which communication interface the vehicle should choose, as well as a vehicle clustering method, in order to improve packet delivery ratio and reduce the number of connections efficiently.

關鍵字(中)

★ 車聯網
★ IEEE 802.11p
★ C-V2X
★ 機器學習
★ 模糊理論

關鍵字(英)

★ Vehicular communication
★ IEEE 802.11p
★ C-V2X
★ Machine Learning
★ Fuzzy Theory

論文目次

摘要 I
ABSTRACT II
目錄 IV
圖目錄 VI
表目錄 IX
1 第一章緒論 1
1.1 研究背景 1
1.2 研究動機與目的 1
1.3 章節概要 2
2 第二章相關研究背景 3
2.1 車聯網基本介紹 3
2.1.1 C-V2X 基本介紹 4
2.1.1.1 4G C-V2X 4
2.1.1.2 5G C-V2X 8
2.1.2 WAVE/DSRC 基本介紹 10
2.1.3 C-V2X與WAVE/DSRC比較 13
2.2 機器學習(Machine Learning，ML)基本介紹 14
2.2.1 RL基本介紹 15
2.2.1.1 RL演算法介紹 15
2.2.1.2 Deep Q Network (DQN) 16
2.3 模糊理論(Fuzzy theory) 17
2.4 相關文獻 19
3 第三章研究方法 25
3.1 系統架構 25
3.2 利用Fuzzy Theory分群方法 27
3.3 利用DQN選擇通訊介面 31
3.3.1 DQN演算法 32
3.3.2 ε-貪婪演算法(epsilon greedy) 34
4 第四章模擬結果與討論 35
4.1 模擬參數與環境介紹 35
4.2 模擬結果分析 39
4.2.1 PDR的比較 40
4.2.2 PDR隨時間的變化 44
4.2.3 Cluster間Handover的比較 48
4.2.3.1 16-QAM，Sensing Range為500公尺 48
4.2.3.2 16-QAM，Sensing Range為1000公尺 50
4.2.3.3 QPSK，Sensing Range為500公尺 52
4.2.3.4 QPSK，Sensing Range為1000公尺 54
4.2.4 RSSI的比較 56
5 第五章結論 58
6 參考文獻 61

參考文獻

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指導教授

陳彥文(Yen-Wen Chen)

審核日期

2019-7-23

推文