隨著 5G Cellular Vehicle-to-Everything（C-V2X）技術的發展，sidelink 通訊已被視為支援車輛之間直接資料傳輸的重要技術。然而，由於 C-V2V 通訊的傳輸範圍有限，發送車輛可能無法直接將資料傳送至目的車輛。因此，3GPP 提出 sidelink relay 技術，以建立由多個 relay vehicles 與 links 所組成的 sidelink path，使兩輛通訊車輛能夠透過 C-V2V sidelink path 進行通訊。為了解決此問題，本論文提出兩種 Multi-access Edge Computing（MEC）輔助之 C-VRⁿV path discovery 方法，其中 Rⁿ 表示 n 個 relay vehicles。這兩種方法分別為：（i）Discrete C-VRⁿV Path Selection for Multi-Pair Sidelink Transmission（DPS），以及（ii）Step-wise C-VRⁿV Path Exploration for Multi-Pair Sidelink Transmission（SWE）。這兩種方法旨在為多組通訊車輛對尋找合適的 relay-assisted C-VRⁿV paths，使其資料流量能夠透過 C-VRⁿV paths 進行傳輸。在所提出的架構中，MEC server 作為集中式控制器，負責收集各車輛定期回報的 context information 並執行 path discovery。接著，MEC server 可根據車輛定期回報的 context information，定期評估各通訊車輛對之可行 C-VRⁿV paths 的 utility。由於 MEC server 具有全域網路拓樸資訊，因此能夠執行更全面的 path selection。為了評估 links 與 paths，本論文設計一個於 MEC server 中執行的 utility function。該 utility function 所考量的因素包括：（i）link lifetime、（ii）transmission rate、（iii）Sidelink Quality Indicator（SLQI），以及（iv）probability of resource collision，以降低 sidelink resource contention 所造成的影響。對於完整的 C-VRⁿV path，path utility 進一步考量 bottleneck link 以及沿著 path 累積的 probability of resource collision。因此，所提出的 utility function 不僅能評估 C-V2V link 的 utility，也能評估對應之完整 C-VRⁿV path 的 utility。兩種所提出的方法在 path exploration procedure 上有所不同。在 DPS 中，MEC server 會針對每一組通訊車輛對個別探索 candidate C-VRⁿV paths，並根據 utility value 選擇最合適的 path。在 SWE 中，MEC server 則以 step-wise 的方式同時為多組通訊車輛對探索 paths。在此過程中，不同車輛對會在每一步選擇能夠最大化各自 path utility 的 relay vehicles。此外，本論文也引入 heuristic pruning function 與 transmission direction filtering mechanism，以減少不必要的 path exploration，並避免 relay paths 偏離目的車輛方向。模擬結果顯示，DPS 與 SWE 在 throughput、data loss rate、average transmission time、successful path establishment rate，以及 route reliability 等方面皆優於其他比較方法。

With the advancement of 5G Cellular Vehicle-to-Everything (C-V2X) technology, sidelink communication has been considered for supporting direct data transmission among vehicles. However, due to the limited transmission range of C-V2V communication, a sending vehicle may not be able to directly transmit data to its destination vehicle. Thus, the sidelink relay technology is proposed by 3GPP to construct a sidelink path, which contains a number of relay vehicles and sidelinks, such that two communicating vehicles can communicate with each other through a C-V2V sidelink path directly. To address this challenge, this work proposes two Multi-access Edge Computing (MEC)-assisted C-VR^nV, where R^n denotes n relay vehicles, path discovery methods, which are called (i) Discrete C-VR^nV Path Selection for Multi-Pair Sidelink Transmission (DPS) and (ii) Step-wise C-V"R" ^nV Path Exploration for Multi-Pair Sidelink Transmission (SWE). These two methods aim to find suitable relay-assisted C-VR^nV paths for multiple communication pairs, for which each pair of vehicles are communicating with each other, to allow their data traffic to be transmitted through C-VR^nV paths. In the proposed architecture, the MEC server acts as a centralized controller to collect each vehicle’s periodically reported context information and perform path discovery. Then the MEC server can periodically evaluate the utility of feasible C-VR^nV paths for the communication pairs based on vehicle’s periodically reported context information. Since the MEC server maintains a global view of the network topology, it can perform a more comprehensive path selection. To evaluate links and paths, this work designs a utility function executed in the MEC server. The factors that are considered in the utility function are (i) link lifetime, (ii) transmission rate, (iii) Sidelink Quality Indicator (SLQI), and (iv) the probability of resource collision, which can reduce the impact of sidelink resource contention. For a complete C-VR^nV path, the path utility further considers the bottleneck link and the accumulated probability of resource collision along the path. Consequently, the proposed utility function evaluates the utility of C-V2V links and the utility of the corresponding entire C-VR^nV path. The two proposed methods differ in their path exploration procedures. In DPS, the MEC server explores candidate C-VR^nV paths for each communication pair individually and selects the most suitable path based on the utility value. In SWE, the MEC server explores paths for multiple communication pairs simultaneously in a step-wise manner. During this process, different vehicle pairs select relay vehicles that maximize their respective path utility at each step. Furthermore, this thesis introduces a heuristic pruning function and a transmission direction filtering mechanism to reduce unnecessary path exploration and avoid relay paths that deviate from the destination. The simulation results show that both DPS and SWE are better than other compared methods in terms of throughput, data loss rate, average transmission time, successful path establishment rate, and route reliability.

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