車聯網環境下基於行動邊緣計算卸載和多代理人 強化學習的即時影像及中繼傳輸方法

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姓名

宋柏廷(Bo-Ting Song) 查詢紙本館藏

畢業系所

通訊工程學系

論文名稱

車聯網環境下基於行動邊緣計算卸載和多代理人強化學習的即時影像及中繼傳輸方法
(Video Streaming and Relaying with MEC Offloading and Multi-Agent Reinforcement Learning in VANETs)

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至系統瀏覽論文 (2025-8-19以後開放)

摘要(中)

隨著互聯網的興起，車聯網（Vehicular Ad Hoc Networks, VANET）作為智能交通系統的核心組成部分，正逐漸受到關注。利用無線通信技術和先進的車輛感知器，車聯網實現了汽車間及汽車與基礎設施間的即時數據交換，提升了交通系統的安全性和效率。然而，快速都市化導致車輛數量劇增，交通事故頻發，尤其是追撞事故。為解決此問題，本研究提出基於5G網路的車輛影像協作共享功能，通過無線通信和跳點通訊技術延伸車輛影像傳輸範圍，並使用強化學習選擇最佳傳輸對象，提高影像品質。此外，本文引入多接取邊緣計算（Multi-access Edge Computing, MEC）以應對車
載系統計算能力不足導致的延遲問題。MEC將計算資源部署在靠近用戶車輛的位置，減少數據傳輸延遲並降低網路頻寬需求。
本文通過多代理人強化學習架構，多代理人深度確定性策略梯度（Multi-Agent Deep Deterministic Policy Gradient,MADDPG），結合邊緣計算，預測最佳路徑並選擇最合適的協作車輛，實現最佳傳輸效率，提升整體系統的性能和可靠性。最後進行不同強化學習模型之間的效能比較，證明部署多代理人強化學習能使系統得到長期最大報酬。

摘要(英)

With the rise of the IoT, Vehicular Ad Hoc Networks (VANET) have gradually gained attention as a core component of intelligent transportation systems. Utilizing wire
less communication technologies and advanced vehicle sensors, VANET enables real-time data exchange between vehicles and between vehicles and infrastructure, enhancing the safety and efficiency of transportation systems. However, rapid urbanization has led to a dramatic increase in the number of vehicles and frequent traffic accidents, particularly rear-end collisions. To address this issue, this study proposes a vehicle image cooperative sharing function based on 5G networks. By extending the range of vehicle image transmission through wireless communication and multi-hop transmission , and using reinforcement learning to select the best transmission targets, the quality of the images is improved.
In addition, this paper introduces Multi-access Edge Computing (MEC) to address the issues of insufficient computing ability and transmission delays in onboard systems.MEC deploys computing resources close to the user vehicles, reducing data transmissiondelays and less network bandwidth requirements.This paper employs a multi-agent reinforcement learning framework, Multi-Agent Deep Deterministic Policy Gradient (MADDPG), combining edge computing to predict optimal routes and select the most suitable cooperative vehicles, achieving optimal transmission efficiency and enhancing the overall system performance and reliability. Finally,
a performance comparison between different reinforcement learning models is conducted, demonstrating that deploying multi-agent reinforcement learning can achieve long-term
maximum rewards for the system.

關鍵字(中)

★ 車聯網
★ 跳點傳輸,
★ 多接取邊緣計算
★ 計算卸載
★ 強化學習

關鍵字(英)

★ VANET
★ hop transmission
★ multi-access Edge Computing
★ offloading
★ Reinforcement Learning

論文目次

目錄
摘要i
Abstract ii
圖目錄v
表目錄vi
1簡介1
2研究背景與文獻探討4
2.1車聯網. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1.1車用行動通訊網路. . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2車輛間的任務邊緣計算與卸載. . . . . . . . . . . . . . . . . . . . . . . . . 8
2.2.1多接取邊緣架構. . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.2.2 MEC之計算卸載. . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.3機器學習. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.3.1單代理人強化學習. . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.3.2多代理人強化學習. . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3系統架構19
3.1影像共享與跳點傳輸路徑. . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.2車載環境. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.3傳輸能耗. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
3.4數據正規化. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
iii
3.5問題定義. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
4強化學習27
4.1單代理人強化學習. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
4.2多代理人強化學習. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
5實驗與結果分析37
5.1實驗環境. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
5.2實驗方法. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
5.2.1實驗參數設計. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
5.2.2模型參數設計. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
5.3實驗結果. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
5.3.1學習率(α)影響. . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
5.3.2衰減率(γ)影響. . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
5.3.3環境模擬與效能指標分析. . . . . . . . . . . . . . . . . . . . . . . 44
5.3.4傳輸延遲比較. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
5.3.5傳輸耗能比較. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
6結論與未來研究59
7致謝60
參考文獻61

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指導教授

胡誌麟(Chih-Lin Hu)

審核日期

2024-8-20

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