English  |  正體中文  |  简体中文  |  Items with full text/Total items : 69561/69561 (100%)
Visitors : 23056638      Online Users : 334
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version

    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/80981

    Title: 上行免許可稀疏碼多工存取資源配置之研究;Study of Uplink Grant-Free SCMA Resource Allocation
    Authors: 李宗翰;Li, Tsung-Han
    Contributors: 通訊工程學系
    Keywords: 第五代行動通訊技術;大規模機器型通訊;稀疏碼多工存取;CTU;mapping rule;5G;mMTC;SCMA;CTU;mapping rule
    Date: 2019-07-26
    Issue Date: 2019-09-03 15:23:18 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 第五代行動通訊技術(5th generation mobile networks, 5G)中提出了三大應用場景,其中一個為大規模機器型通訊(Massive Machine Type Communications, mMTC),其應用於萬物聯網的場景之下,為了實現此一場景,有人提出了免許可(grant-free)的機制與稀疏碼多工存取的多工方式,在預先定義好的資源上給多個使用者進行傳送,以提升資源使用效率。
    在上行免許可稀疏碼多工存取的場景下,用戶設備將根據mapping rule選取CTU(Contention transmission Unit)進行傳送,而當多個UE選取相同CTU進行上傳時便會發生碰撞。為了降低碰撞率並提升傳輸效率,本篇論文提出了兩階段CTU分配方式(Two-stage CTU Allocation, TCA),期望可以透過此一方式確認用戶設備真的有資料要傳送再讓UE獨占CTU。另外,本論文也嘗試結合TCA與機器學習來分配資源,希望能夠進一步改善傳輸的效率。
    ;There are three main uses cases for 5th generation mobile networks (5G), one of the cases is Massive Machine Type Communications (mMTC), which is applied to Internet of Things. To implement this scenario, someone has proposed the grant-free transmission and Sparse Code Multiple Access (SCMA), that means users can transmit data over the predefined resource to improve the resource usage.
    In uplink grant-free SCMA transmission, UE has to choose CTU to uplink data according to mapping rule. The CTU would collided when more than two UEs choose the same CTU to uplink data. To reduce collision rate and improve transmission efficiency, this thesis proposed Two-stage CTU Allocation method (TCA), which intend to make UEs own dedicated CTU only when the UEs has data to transmit. Hoping to have better results, this thesis also attempts to combine TCA with machine learning for resource allocation.
    According to the simulation of this thesis, TCA has good performance in several aspects, and it can have better performance in situation with higher traffic load. However, the improvement of resource allocation by using TCA with machine learning is suitable for scenarios with lower number of UEs and its improvement is limited.
    Appears in Collections:[通訊工程研究所] 博碩士論文

    Files in This Item:

    File Description SizeFormat

    All items in NCUIR are protected by copyright, with all rights reserved.

    社群 sharing

    ::: Copyright National Central University. | 國立中央大學圖書館版權所有 | 收藏本站 | 設為首頁 | 最佳瀏覽畫面: 1024*768 | 建站日期:8-24-2009 :::
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback  - 隱私權政策聲明