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研究生: 蔡承諭
Cheng-Yu Tsai
論文名稱: 基於強化學習之URLLC上行資源配置方法研究
Study of Reinforcement Learning for Resource Allocation on URLLC uplink
指導教授: 陳彥文
Yen-Wen Chen
口試委員:
學位類別: 碩士
Master
系所名稱: 資訊電機學院 - 通訊工程學系
Department of Communication Engineering
論文出版年: 2020
畢業學年度: 108
語文別: 中文
論文頁數: 70
中文關鍵詞: 5G行動通訊高可靠低延遲傳輸無允諾上行封包重傳強化學習
外文關鍵詞: 5G Mobile communication, URLLC, Grant-free uplink, packet retransmissions, Reinforcement Learning
相關次數: 點閱:11下載:0
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  • 近幾年對於第五代行動通訊(5G)的標準規格,國際組織3GPP持續的在進行訂制,5G可以分成三大應用場景,分別是增強型行動寬頻通訊(Enhanced Mobile Broadband, eMBB),超可靠度和低延遲通訊(Ultra-reliable and Low Latency Communications, URLLC),以及大規模機器型通訊(Massive Machine Type Communications, mMTC),其中URLLC為了將來能應用在工業自動化生產、無人駕駛等等,極高的可靠度(high reliability)與極低的時間延遲(low latency)便是他所追求的目標,然而如此嚴格的需求將對現有網路通訊系統帶來巨大的挑戰,因此本篇論文將焦點放在URLLC的資源分配上。
    對於要上行的突發性URLLC流量採用無允諾上行(Uplink Grant-free)方式可以有效減少用戶設備(User Equipment, UE)與基地台(Base Station, BS)之間的授權延遲,而一般基地台會分配給URLLC的UE專用資源(dedicated resource)或共享資源池(shared resource pool)進行上行,無論使用哪種資源皆各有優缺點存在,因此本篇論文將依據URLLC流量特性對UE分組,在頻寬有限的情況下做最有效益的資源配置,並搭配強化學習的方式決定共享資源池的大小。從模擬結果可以看出,在各種情況下皆能有效節省資源,同時達到高可靠度與低延遲的目標,尤其是高負載的情況更能體現優勢。


    In recent years, the international organization 3GPP has continued to customize the standard specifications of the fifth generation mobile communication (5G). 5G can be divided into three major application scenarios, namely Enhanced Mobile Broadband (eMBB), Ultra-reliable and Low Latency Communications (URLLC), and Massive Machine Type Communications (mMTC). URLLC can be used in industrial automation production, unmanned driving, etc. in the future. The purposes of URLLC is high reliability and low latency. However, such strict requirements will bring great challenges to existing network communication systems, so this paper will focus on the resource allocation of URLLC.
    For the burst URLLC traffic to be uplinked, the Uplink Grant-free method can effectively reduce the request delay between the UE and the BS. BS may allocate the dedicated resource or shared resource pool to URLLC UE for uplink. No matter which resource is used, there are strength and weaknesses. Therefore, this paper will group UEs according to the traffic characteristics. Under limited bandwidth, it will make the most effective resource allocation, and use reinforcement learning to determine the size of the shared resource pool. From the simulation results, it shows that the resources can be effectively saved in various situations, and achieve the goals of high reliability and low latency at the same time, especially in high load case.

    摘要 I Abstract II 致謝 III 目錄 IV 圖目錄 VI 表目錄 VIII 1. 第一章 緒論 1 1.1. 研究背景 1 1.2. 研究動機與目的 1 1.3. 章節概要 2 2. 第二章 相關研究背景 3 2.1. 5G三大場景介紹 3 2.2. 5G訊框結構 4 2.2.1. 子載波間距與時槽配置 4 2.2.2. 迷你時槽的設計 6 2.2.3. 自含式(Self-contained)子訊框架構 6 2.3. Grant-free傳輸機制 7 2.4. 多基地台連結 9 2.5. 重覆性傳送(Repetition) 10 2.6. 機器學習介紹 10 2.6.1. Q-learning 12 2.6.2. Deep Q Network 12 2.7. 相關文獻 14 3. 第三章 研究方法 20 3.1. 系統架構 20 3.2. 系統流程 21 3.2.1. 系統參數 21 3.2.2. DQN架構流程 23 3.2.3. Reward function介紹 26 3.2.4. gNB系統流程 27 3.2.5. UE端流程 29 4. 第四章 模擬結果與討論 30 4.1. 模擬環境介紹 30 4.2. 模擬結果分析 33 5. 第五章 結論 54 6. 參考文獻 56

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