機器型態通訊技術支援各式各樣的機器對機器的相關應用，而對於這些應用，節省能
源消耗是一項非常重要的議題；此外，機器型態通訊技術同時也被預期能夠支援物連
網服務，而隨著物聯網裝置數量的急遽成長，吞吐量以及封包延遲等服務品質的指標
也成為重要的議題。過去已經有許多上行排程演算法在討論這些議題，然而這兩種議
題是需要權衡的，降低能源消耗的話，服務品質就會下降，反之亦然。但是我們利用
多重存取邊端運算技術提出一個新的上行排程演算法，可以在降低能源消耗的同時盡
可能提高服務品質。為了評估我們提出的演算法的效能，利用OpenAirInterface作為實
作與模擬平台進行開發並收集實驗數據，除此之外，我們還實作輪循以及部分公平演
算法作為比較用的演算法。最後，我們針對實驗結果進行分析，並且實驗結果說明我
們提出的演算法具有較好的效能。此外，我們也有探討多重存取邊端運算技術所提供
的運算能力對於系統效能的影響。

Machine Type Communications (MTC) supports various Machine-to-Machine (M2M) ap-
plications, where the energy eciency is a key issue. In addition, MTC is expected to
support a variety of Internet of Things (IoT) services, and the number of IoT devices
is rapidly growing which causes the improvement of the system performance is neces-
sary. Many uplink scheduling algorithms consider the power consumption or aim to the
system performance, but not both. In this thesis, we propose an scheduling algorithm
assisted by a emerging technology, Multi-access Edge Computing (MEC), and our algo-
rithm minimizes the energy consumption and improves the system performance as much
as possible. In order to evaluate our algorithm, we use LTE as an example and OpenAir-
Interface (OAI) as a testbed, because the simulation platform of LTE is fully developed
and more reliable. In this thesis, the overview of OAI scheduler is introduced, and the
implementation of our algorithm using OAI are detailed. Compared with Round-Robin
(RR) and Proportional fair (PF) algorithm, our performance study shows that our al-
gorithm improves the performance in terms of the energy consumption and the system
performance. Moreover, through the simulation results, we analyze the relation between
the computational capability of the MEC server and the system performance.

摘要
Abstract
Contents
List of Figures
List of Tables
1 Introduction------------------------------------1
2 Related Work------------------------------------5
3 System Model and Problem Formulation------------7
3.1 System model----------------------------------7
3.2 Problem Formulation--------------------------10
4 Scheduling Algorithm---------------------------13
4.1 Concept of the Algorithm---------------------13
4.2 Procedure of the Algorithm-------------------14
5 OAI Simulator Overview-------------------------18
5.1 OAI-SIM protocol stack-----------------------18
5.1.1 PHY----------------------------------------18
5.1.2 MAC----------------------------------------19
5.1.3 OTG----------------------------------------19
5.2 Data structures------------------------------20
5.2.1 UE template--------------------------------20
5.2.2 UE list------------------------------------20
5.2.3 MAC xface----------------------------------21
5.3 Functions------------------------------------21
5.3.1 eNB dlsch ulsch scheduler()----------------21
5.3.2 schedule ulsch rnti()----------------------22
5.3.3 ulsch scheduler pre processor()------------22
5.4 Scheduling procedure-------------------------22
6 Implementation of Algorithm Using OAI-SIM------27
6.1 Logical entities of the testbed--------------27
6.2 Implementation of the scheduler--------------28
6.2.1 Data structures and variables--------------29
6.2.2 Functions----------------------------------31
6.3 Implementation of the MEC server-------------32
6.3.1 Data structures and variables--------------32
6.3.2 Functions----------------------------------33
6.4 Scheduling procedure-------------------------35
7 Performance Evaluation-------------------------37
7.1 Simulation parameters------------------------37
7.2 Scenario and OSD-----------------------------38
7.3 Simulation results---------------------------39
8 Conclusion-------------------------------------43

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