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研究生:袁傑宇
研究生(外文):Jie-Yu Yuan
論文名稱:基於馬可夫決策鏈在第五代行動通訊網路之有效頻譜排程機制
論文名稱(外文):An Efficient Spectrum Scheduling mechanism by using Markov Decision Chain in 5G
指導教授:趙涵捷趙涵捷引用關係
指導教授(外文):Han-Chieh Chao
口試委員:趙涵捷陳旻秀陳麒元卓信宏曾繁勛
口試委員(外文):Han-Chieh ChaoMin-Xiou ChenChi-Yuan ChenHsin-Hung ChoFan-Hsun Tseng
口試日期:2019-07-20
學位類別:碩士
校院名稱:國立東華大學
系所名稱:電機工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:中文
論文頁數:44
中文關鍵詞:5G雲端無線電接入網路任務排程馬可夫鏈粒子群演算法
外文關鍵詞:5GCloud Radio Access NetworkTask schedulingMarkov chainParticle swarm optimization
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近年來,第五代行動通訊系統(5G)逐漸成為一項熱門議題被廣泛地討論。5G網路將為異質性網路(Heterogeneity Network, HetNet)架構,以便向下相容更多網路環境,諸如:密集性網路(Ultra Dense Network, UDN)、蜂巢式網路(Cellular Network)以及機器對機器(Machine to Machine,M2M)溝通等,上述所提之通訊技術容易使得網路資源難於管理。同時,資源分配於無線網路環境中一直為重要議題,儘管於5G網路中所使用頻率更高和更大的頻寬。考慮到頻譜仍是有限資源,但多數使用者皆希望能夠取得最佳服務品質,故該如何進行頻譜資源的分配及管理,成為5G網路環境裡重大問題。
雲端無線接取網路(Cloud Radio Access Network,C-RAN)或許是解決此項問題有效方法之一,雲端無線電接取網路有別於傳統網路架構,於此架構下之基頻單元 (Baseband Unit, BBU)與無線寬頻頭端設備(Remote Radio Head ,RRH)會從基地台(Macro Base Station, MBS)中獨立出來,除此之外,多個基頻單元將會構成基頻單元池(Baseband Unit pool, BBU pool),藉由基頻單元池能夠達成資源集中化,使整體網路資源更加便於管理。
為能夠有效解決資源分配問題使其頻譜能夠達到最大利用率,並且使整體網路能源使用率能夠最佳。有鑑於在5G網路環境中,行動裝置的增加以及流量的迅速成長,本文將針對使用者所發出之任務請求進行排程,並以馬可夫鏈(Markov Chain)對頻道狀態進行初始化排列,最後,為有效進行任務排程以達到目標最小化能源效率,將利用修改後之粒子群演算法針對任務排程進行最佳化。為驗證本研究所提出方法之效果,本文利用模擬進行實驗驗證,並與隨機排程及先進先出演算法進行比較。由結果能夠得知,利用所提出方法確實能夠對所請求之任務進行有效地排程;同時,也能夠有效降低整體能源消耗。
Recently, the 5th generation mobile networks systems 5G is a popular issue and discussed widely. The architecture of 5G is heterogeneity network (HetNet), and it can support more network environment, like the ultra-dense network (UDN), traditional cellular network and Machine to Machine (M2M) communication. Therefore, the network resource management will be difficult. Resource allocation is an important issue at the wireless network, although the high frequency and bigger bandwidth are used. Consider the spectrum resource is limited, but almost user with the hope that equipment (UEs) can get the better quality of services (QoS), how to manage the spectrum resource and allocation is a big problem in 5G.
The cloud radio access network (C-RAN) maybe one of the promising solutions for this problem and it is different from the traditional wireless mobile network. The baseband unit (BBU) and radio remote head (RRH) will be independent on the base station (BS). However, when many BBU are integrated it can be a BBU pool, and by doing this, BBU pool can easily manage the network resource and improve the energy-efficiency. According to fast grown-up devices and traffic, we will focus on task scheduling with UEs.
To solve the problem of resource allocation and maximum the energy-efficiency, we propose the use of Markov chain to predict the channel state. On the other hand, the task scheduling the modified particle swarm optimization (MPSO) will be used in this thesis to find the best scheduling. In this thesis, we will use the simulation to verify the performance for our proposed mechanism and compare with random and first in first out. The result shows our mechanism can be scheduled the task efficiently; at the same time, it can reduce total power consumption too.
第一章 緒論 1
第二章 背景知識 6
第三章 問題定義 12
第四章 基於馬可夫鏈頻道狀態預測結合MPSO之任務排程 18
第五章 實驗模擬結果 25
第六章 結論 37
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