URLLC與mMTC共存之上行免許可稀疏碼多工存取資源配置研究

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姓名

李科進(Ke-Chin Lee) 查詢紙本館藏

畢業系所

通訊工程學系

論文名稱

URLLC與mMTC共存之上行免許可稀疏碼多工存取資源配置研究
(Study of Uplink Grant-Free SCMA Resource Allocation for URLLC and mMTC Coexistence)

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摘要(中)

近幾年第五代行動通訊（5G）蓬勃發展，國際電信聯盟（International Telecommunication Union, ITU）將5G主要規範為三大應用場景，包含增強型行動頻寬（Enhanced Mobile Broadband, eMBB）、超可靠度和低延遲通訊（Ultra-reliable and low latency communications, URLLC）以及大規模機器通訊（Massive machine type communications, mMTC），其中URLLC可應用在車聯網等強調低延遲高可靠的即時傳輸，mMTC可應用在智能城市等大量物聯網設備的環境，由於網路資源數量有限，且兩者要求目標不同，在目前通訊網路系統中要如何讓這兩種不同類型的設備達到更好的性能有著巨大的挑戰。
上行業務以稀疏代碼多重連接（Sparse Code Multiple Access, SCMA）的場景下，將用戶設備（User Equipment, UE）選取競爭傳輸單元（Contention Transmission Unit, CTU），並採無允諾上行（Uplink Grant-free）方式來降低基地台（Base Station, BS）與UE間的授權延遲。此外，會基於mapping rule分配方式競爭選取CTU，本論文為了滿足URLLC低延遲及高可靠的特性提出了步移增強式mapping rule（Enhanced Mapping Rule with Step Movement, EMRSM），為了使mMTC更有效率的使用資源提出了多階段之CTU分配（Multi-stage CTU Allocation, MCA），並利用強化式學習動態調整CTU資源來探討URLLC及mMTC的共存方案。從模擬結果可看出，EMRSM可滿足URLLC低延遲高可靠的要求，利用MCA可看出大量的封包訪問網路不會造成壅塞，將強化式學習動態調整CTU資源用在mapping rule分配方法下，可些微提升成功率，然而由於EMRSM成功率很高，因此強化式學習動態調整CTU資源用在EMRSM分配方法下，反而降低了成功率。

摘要(英)

In recent years, the fifth generation of mobile communications (5G) has flourished. The International Telecommunication Union (ITU) has standardized 5G into three major application scenarios, including eMBB, URLLC and mMTC. URLLC can be applied to the Internet of Vehicles and other instant transmissions that emphasize low latency and high reliability. The mMTC can be applied to the environment of a large number of Internet of Things devices such as smart cities. However, the number of network resources is limited, and the requirements of the two are different. How to coexist these two types of equipment with different requirements is a huge challenge to the current communication network system.
In SCMA transmission, Contention Transmission Unit (CTU) is selected by UE. The Uplink Grant-free method is adopted to reduce the authorization delay between BS and UE. In addition, the CTU will be selected based on the mapping rule allocation method. This paper proposes Enhanced Mapping Rule with Step Movement (EMRSM) in order to meet the low-latency and high-reliability characteristics of URLLC. Multi-stage CTU Allocation (MCA) is proposed in order to make mMTC use resources more efficiently. And using reinforcement learning to dynamically adjust CTU resources to explore the coexistence of URLLC and mMTC. According to the simulation results that EMRSM can meet the requirements of URLLC for low latency and high reliability. A large number of packets accessing the network will not cause congestion by MCA. Dynamic adjustment of CTU resources with reinforcement learning for mapping rule can slightly increase the success rate. However, dynamic adjustment of CTU resources with reinforcement learning for EMRSM actually reduces the success rate because the EMRSM success rate is quite high.

關鍵字(中)

★ 5G
★ 稀疏代碼多重連接
★ CTU
★ 無允諾上行
★ mapping rule
★ 強化式學習

關鍵字(英)

★ 5G
★ SCMA
★ CTU
★ Grant-free
★ mapping rule
★ Reinforcement Learning

論文目次

摘要 I
ABTRASCT II
致謝 III
目錄 IV
圖目錄 VI
表目錄 IX
1. 第一章緒論 1
1.1. 研究背景 1
1.2. 研究動機與目的 2
1.3. 章節概要 2
2. 第二章相關研究背景 4
2.1. 5G三大場景介紹 4
2.1.1. URLLC關鍵效能指標 5
2.1.2. mMTC關鍵效能指標 6
2.2. 5G訊框架構 6
2.3. 稀疏代碼多重連接（SPARSE CODE MULTIPLE ACCESS, SCMA） 9
2.4. GRANT-FREE傳輸機制 11
2.5. 重覆性傳送（REPETITION） 13
2.6. 機器學習介紹 15
2.6.1. 強化式學習介紹 16
2.7. 相關文獻 18
3. 第三章研究方法 22
3.1. 系統架構 22
3.2. CTU分配 23
3.2.1. 步移增強式mapping rule（Enhanced Mapping Rule with Step Movement, EMRSM） 23
3.2.2. 數學模型 25
3.2.3. 多階段之CTU分配（Multi-stage CTU Allocation, MCA） 27
3.3. 結合機器學習之動態CTU分配 32
4. 第四章模擬結果與討論 35
4.1. 模擬參數及環境介紹 35
4.2. 模擬結果分析 37
4.2.1. mapping rule與EMRSM比較 37
4.2.2. MCA模擬結果分析 43
4.2.3. 動態調整CTU之模擬結果分析 49
5. 第五章結論 54
6. 參考文獻 55

參考文獻

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指導教授

陳彥文

審核日期

2021-8-13

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