第五代行動網路主要有三種服務類型,分別是:大規模機器型通訊、超可靠度和低延遲通訊、以及增強型行動寬頻通訊。一個典型的大規模機器型通訊服務是智慧電網中的智慧讀表,其中智慧電表會將量測資料經由智能電表基礎建設網路傳送到集中器,集中器再將整合後的量測資料透過第五代行動網路送到後端的資料中心。由於集中器的單價較高並且需要滿足延遲與可靠度的需求,在建置智能電表基礎建設網路時,決定集中器放置位置與數量是一個很重要的問題。另一方面,許多超可靠度和低延遲通訊服務需要提供使用者裝置平順的使用者體驗,除此之外,當使用者
裝置從一個基地台的覆蓋範圍移動到另一基地台的覆蓋範圍時,使用者裝置需要執行換手程序來轉換服務基地台。因此,一個為移動的使用者裝置設計的換手方法必須要能夠防止發生斷線與非必要換手。然而,傳統的第四代行動網路換手方法只適用於只存在大基地台的行動網路,並無法直接套用到第五代行動異質網路上,原因在於小基地的隨機分佈使得使用者裝置的信號與干擾加噪聲比變化是不規則的,這可能導致斷線與非必要換手次數增加。再者,增強型行動寬頻通訊服務的目標是要提供使用者裝置較高的傳輸速率。因此設計一個有效率的換手方法在第五代行動網
路中選擇目標基地台使得使用者裝置的平均吞吐量最大化在最近受到很廣泛的關注。因此在本論文中,我們將提出針對上述三個問題的有效率方法。在第一部份,為了最小化集中器放置成本與滿足延遲與可靠度需求,我們研究了第五代行動網路中建構智能電表基礎建設的集中器放置問題,並且提出了對應的三階段式方法。在第二部份,為了最小化斷線次數並同時減少非必要換手次數,我們研究了在第五代行動異質網路中決定換手時機的換手問題。在第三部份,為了最大化使用者裝置的平均吞吐量,我們研究了在第五代行動網路中選擇目標基地台的換手問題。後兩個
問題由於複雜的網路結構很難去表示成最佳化問題,因此我們分別基於模糊邏輯與線性信賴上限設計了換手方法來決定換手時機與選擇目標基地台。總而言之,本論文包含了三個主題,分別是(一)設計三階段式方法來放置集中器使得成本最小並滿足延遲與可靠度需求,(二)設計基於模糊邏輯的換手方法來決定換手時機使得最小化斷線次數與減少非必要換手,(三)設計基於線性信賴上限的換手方法來選擇目標基地台使得使用者裝置的平均吞吐量最大。最後,針對每一個主題,我們的模擬實驗顯示我們所提出的方法相較於現今先進的方法有良好的效能表現。

The fifth-generation (5G) mobile network has three distinctive classes of use cases: massive machine type communication (mMTC), ultra-reliable low-latency communication (URLLC), and enhanced mobile broadband (eMBB). A typical mMTC service is the smart metering in a smart grid, where smart meters send metering data to concentrators via the advanced metering infrastructure (AMI) network, and concentrators send the collected metering data to the utility provider data center via the 5G network. Due to the high cost of concentrators and the need of the satisfaction of the latency and reliability requirements, multiple concentrator placement is a critical problem for the establishment of an AMI network. On the other hand, many URLLC services require to provide smooth user experience for user equipments (UEs). In addition, when a UE moves from the radio coverage of one base station (BS) to that of another one, the UE needs to perform a handover between these two BSs. Thus, a handover algorithm for moving UEs should be able to prevent disconnections and unnecessary handovers. However, a traditional 4G handover algorithm of handover timing determination that performs well in a macro-cell-only network could not be employed in the 5G heterogeneous network with the random distribution of small BSs due to the irregular change of the signal-to-interference-plus-noise ratio (SINR) of a moving UE, which might result in the increase of the disconnections and unnecessary handovers. Furthermore, an eMBB service in a 5G network needs a high data rate. As a result, the development of efficient handover algorithms to select the target (serving) BS such that the average throughput of UEs is maximized in 5G networks has received much attention. Thus, in this dissertation, we aim to propose three efficient algorithms to address the above three issues in three parts, respectively. In the first part, to minimize the cost of the placed concentrator while satisfying the latency and reliability requirements, we study the multiple concentrator placement problem in an AMI network within a 5G network and propose a three-phase algorithm of cluster construction, sink selection, and disjoint cluster establishment. In the second part, to minimize the number of disconnections and reduce the number of unnecessary handovers, we address the handover problem of handover timing determination in a 5G heterogeneous network. In the last part, to maximize the average throughput of a UE, we tackle the handover problem of the target BS selection in a 5G network. Since the second and last problems are difficult to formulate as an optimization problem due to the complex network architectures, we design the handover algorithms of handover timing determination and target BS selection based on the fuzzy logic and linear upper confidence bound (LinUCB), respectively. All in all,
our three major topics in this dissertation include: (A) The design of a three-phase algorithm for the multiple concentrator placement such that the cost is minimized and the latency and reliability requirements are satisfied. (B) The development of a fuzzy-logic-based handover algorithm of handover timing determination such that the number of disconnections is minimized while the number of unnecessary handovers is reduced. (C) The development of a LinUCB-based handover algorithm of target BS selection such that the average throughput of a UE is maximized. And, in each topic, simulations show that our proposed algorithm performs well as compared to the state-of-the-art methods in terms of the performance metrics.

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