於5G/後5G行動網路通訊(Beyond 5G Mobile Communication Network, B5G)中，3GPP共定義三大技術場景要求，需要符合低延遲高可靠(ultra-reliable and low latency communication, URLLC)、增強寬頻通訊(enhanced mobile broadband, eMBB)以及海量機器通訊(Massiv machine type communication, mMTC)。其中車聯網(Internet of Vehicle, IoV)於B5G中逐漸成為一項重要的議題，因需確保使用者的安全，故IoV技術須滿足URLLC；同時，IoV也因需要支援車間通訊(Vehicle to Vehicle, V2V)，故也將mMTC概念融入其中。本文將著重於IoV動態環境中，並針對IoV與URLLC所產生之問題進行探討。
IoV除應支援V2V外，也需支援車載對基礎設施(Vehicle to Infrastructure, V2I)。本研究於蜂巢式車間通訊(Cellular Vehicle to Vehicle, C-V2V)環境下，對於頻道的資源分配將會有兩種模式，分別為配置模組排程-1(mode-1 configuration scheduling)-基地台(Base station, BS)集中分配以及配置模組排程-2(mode-2 configuration scheduling)-由車載自由選取合適的子頻道進行接取。於C-V2V環境下，能先利用模組-1對所有接取的VUE選擇合適的子頻道並進行排程，後續則利用模組-2進行動態排程。
由於5G/B5G採取更高的頻率提高整體傳輸能力，但同時整體覆蓋範圍也隨之降低，故需藉由小細胞基地台(Small cell, SC)協助傳輸。由於VUE移動時，將會頻繁的接取BS或小細胞基地台(Small cell, SC)，進而導致負載不均的情形發生，故對於換手(Hnadover, HO)後的BS或SC選擇將成為研究重點。本研究首先將會針對車載的水平換手及垂直換手進行討論，並運用位置預測及元啟發式演算法(Metaheuristic algorithm)設計HO決策，以讓整體環境BS及SC呈現負載平衡的狀態。隨後，頻譜資源的分配將根據3GPP所提出之兩種分配模式進行研究，於模組配置-1將先利用元啟發式演算法進行子頻道(Sub-channel)的資源排程，於模組配置-2則運用合作式的非零合賽局，讓VUE能夠選擇合適的子頻道，以保持通訊延遲以及傳輸吞吐量。

In the 5G/ Beyond 5G Mobile Communication Network (B5G), 3GPP defines three technical scenario requirements, namely ultra-reliable and low latency communication (URLLC), enhanced mobile broadband (eMBB), and massive machine-type communication (mMTC). The Internet of Vehicles (IoV) has gradually become a critical issue in B5G. For driver safety, IoV technology must meet URLLC. At the same time, the IoV also needs to support Vehicle to Vehicle (V2V). Therefore, the technology of mMTC must also include. We will focus on the dynamic environment of IoV and discuss the issues of URLLC.
In addition to supporting V2V, IoV also needs to assist the vehicle with infrastructure (V2I). This study will consider the cellular vehicle to vehicle (C-V2V) and two configurations for channel resource allocation. First are resource allocation mode-1 configurations - Base station (BS) centralized allocates the sub-channel resource for each VUE. And mode-2 configurations – the VUE will choose the best sub-channel to access. In the C-V2V, the BS will first select appropriate sub-channels and scheduling for all accessed VUEs and then switch to mode-2.
Since 5G/ B5G adopts higher frequencies to improve the overall transmission capacity, at the same time, the coverage has also reduced; hence, it is necessary to use a small cell (SC) to assist in transmission. When the VUE moves, it will frequently access the BS or SC, resulting in unbalanced loading. Therefore, the study of soft handover strategies has become more critical in recent years. This research will first discuss the horizontal and vertical handover of vehicle and the deployment of the location prediction and metaheuristic algorithm to design the HO strategy so that the BS and SC are in a state of load balance. To solve the spectrum resource allocation, we will follow the 3GPP standard. In mode-1, the BS/SC will use the improve the particle swarm optimization (PSO) for the task scheduling. In mode-2, a cooperative non-zero-sum game allows VUE to select appropriate sub-channels to maintain communication latency and transmission throughput.

第一章 Introduction 1
第一節 Motivation and Goal of the research 6
第一小節 Handover Strategy 6
第二小節 Internet of Vehicle 7
第二節 Contribution 13
第三節 Dissertation Organization 15
第二章 Background and Related Work 17
第一節 Overview of Handover Process 17
第二節 Overview of Metaheuristic Algorithm 20
第三節 Related Work 25
第三章 System Model and Problem Definition 31
第一節 System Model 31
第二節 Problem Definition 35
第一小節 C-V2V HO Problem 35
第二小節 C-V2V Channel Resource Allocation Problem 39
第四章 Efficient C-V2V Resource Allocation Mechanism 45
第一節 HO Strategy 45
第一小節 Location Prediction for VUE 45
第二小節 HO Strategy 47
第二節 Resource Allocation for Mode-1 Configuration in C-V2V 55
第一小節 Channel State Prediction based on Markov Chain 55
第二小節 Task scheduling based on Improved PSO 57
第三節 Resource Allocation for Mode-2 Configuration in C-V2V 61
第五章 Simulation Results and Analysis 67
第一節 Simulation Setting 67
第二節 Simulation Results and Analysis 69
第一小節 The Results for HO Strategy 69
第二小節 Mode-1 Configuration Task Scheduling 78
第三小節 Mode-2 Configuration Dynamic Scheduling 87
第四小節 Summary of Simulation Experiments 91
第六章 Conclusion 93

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