博碩士論文 107523027 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:177 、訪客IP:3.144.143.31
姓名 蔡承諭(Cheng-Yu Tsai)  查詢紙本館藏   畢業系所 通訊工程學系
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)
相關論文
★ 應用MSPP至DWDM都會光纖網路的設計★ 光網路與WiMAX整合架構研究及其簡化雛型實驗
★ 以Linux系統為基礎之NAT效能優化研究及其實作★ 光波長劃分多工網路之路徑保護機制研究
★ 標籤交換網路下具有服務品質路由安排之研究★ 以訊務相關性為基礎的整合性服務可調整QoS排程器之研究
★ 以群體播送支援IPv6環境下移動式網路連結更新之研究★ 無線區域網路資源動態分配之效能研究
★ 在微觀移動環境下有效資源保留之路徑管理研究★ 無線網路交握程序之預先認證方法分析與比較
★ 無線區域網路虛擬允入控制之研究★ IPv6環境下移動網路之連結更新程序及其效能之研究
★ 具有限數量波長轉換節點的分波多工網路之群播波長分配與容量計算研究★ 階層化行動式IPv6移動錨點選擇機制研究
★ 具高能量移動節點之叢集式感測網路 效能研究★ 預先註冊之快速換手階層化行動式IPv6研究
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   [檢視]  [下載]
  1. 本電子論文使用權限為同意立即開放。
  2. 已達開放權限電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。
  3. 請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。

摘要(中) 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。
摘要(英) 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.
關鍵字(中) ★ 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
關鍵字(英) ★ 5G Mobile communication
★ URLLC
★ Grant-free uplink
★ packet retransmissions
★ Reinforcement Learning
論文目次 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
參考文獻 [1] ITU, "IMT Vision – Framework and overall objectives of the future development of IMT for 2020 and beyond," 09 2015. [Online]. Available: https://www.itu.int/dms_pubrec/itu-r/rec/m/R-REC-M.2083-0-201509-I!!PDF-E.pdf. [Accessed 10 06 2019].
[2] 3GPP TR 38.913, “Study on scenarios and requirements for next generation access technologies,” 3GPP Tech. Rep., V15.0.0, Jul. 2018.
[3] 3GPP TR 38.211, “NR; Physical channels and modulation,” 3GPP Tech. Rep., V16.0.0, Dec. 2019.
[4] [Online]. Available: https://www.sharetechnote.com/html/5G/5G_FrameStructure.html.
[5] 3GPP TR 38.912, “Study on New Radio (NR) access technology,” 3GPP Tech. Rep., V15.0.0, Jul. 2018.
[6] [Online]. Available: http://howltestuffworks.blogspot.com/2019/11/5g-nr-time-domain-slots-and-slot-formats.html.
[7] 3GPP TS 38.213, “NR; Physical layer procedures for control,” 3GPP Tech. Rep., V16.1.0, Apr. 2020.
[8] [Online]. Available: https://blogs.keysight.com/blogs/inds.entry.html/2018/09/07/5g_flexible_numerolo-XHto.html.
[9] 3GPP TS 38.331, “NR; Radio Resource Control (RRC); Protocol specification,” 3GPP Tech. Rep., V16.0.0, Apr. 2020.
[10] [Online]. Available: http://www.techplayon.com/5g-nr-grant-free-dynamic-scheduling-transmission-without-grant-twg/.
[11] Azad Ravanshid et al., “Multi-connectivity functional architectures in 5g,” in 2016 IEEE International Conference on Communications Workshops (ICC), May, 2016.
[12] [Online]. Available: https://www.accton.com/Technology-Brief/the-emergence-of-5g-mmwave/.
[13] [Online]. Available: https://blogs.nvidia.com.tw/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/.
[14] [Online]. Available: https://blogs.oracle.com/datascience/reinforcement-learning-deep-q-networks.
[15] [Online]. Available: https://medium.com/%E9%9B%9E%E9%9B%9E%E8%88%87%E5%85%94%E5%85%94%E7%9A%84%E5%B7%A5%E7%A8%8B%E4%B8%96%E7%95%8C/%E6%A9%9F%E5%99%A8%E5%AD%B8%E7%BF%92-ml-note-reinforcement-learning-%E5%BC%B7%E5%8C%96%E5%AD%B8%E7%BF%92-dqn-%E5%AF%A6%E4%BD%9Catari-game-7f9185f.
[16] 3GPP TS 22.104, “Service requirements for cyber-physical control applications in vertical domains,” 3GPP Tech. Rep., V17.1.0, Sep. 2019.
[17] R1-1705463, “UL grant-free transmission for URLLC,” 3GPP TSGRAN WG1 #88, Apr. 2017.
[18] Zhiyi Zhou, Rapeepat Ratasuk, Nitin Mangalvedhe, and Amitava Ghosh, "Resource Allocation for Uplink Grant-Free Ultra-Reliable and Low Latency Communications," IEEE 87th VTC Spring, 2018.
[19] Salah Eddine Elayoubi, Patrick Brown, Matha Deghel, and Ana Galindo-Serrano, "Radio Resource Allocation and Retransmission Schemes for URLLC Over 5G Networks," IEEE Journal on Selected Areas in Communications, April, 2019.
[20] Nurul Huda Mahmood et al., "Uplink Grant-Free Access Solutions for URLLC services in 5G New Radio," International Symposium on Wireless Communication Systems, 2019.
[21] Luca Buccheri et al., "Hybrid Retransmission Scheme for QoS-defined 5G Ultra-Reliable Low-Latency Communications," IEEE Wireless Communications and Networking Conference, 2018.
[22] R1-1705654, “UL grant-free transmission for URLLC,” 3GPP TSG RAN WG1 #88, Apr. 2017.
[23] S. Xing, et.al, “Advanced Grant-free Transmission for Small Packets URLLC Services”, International Conference on Communications Workshops (ICC Workshops), 2019.
[24] 3GPP TS 38.212, “NR; Multiplexing and channel coding,” 3GPP Tech. Rep., V16.1.0, Mar. 2020.
指導教授 陳彥文(Yen-Wen Chen) 審核日期 2020-8-4
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明