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https://nccur.lib.nccu.edu.tw/handle/140.119/153916
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| Title: | 用於優化5G核心網路上行鏈路流量轉發的聯邦學習之研究
A Study on Federated Learning for Optimizing Uplink Traffic Forwarding in 5G Core Networks |
| Authors: | 黃奕晟
Huang, Yi-Cheng |
| Contributors: | 蔡子傑
Tsai, Tzu-Chieh
黃奕晟
Huang, Yi-Cheng |
| Keywords: | 網路功能虛擬化
軟體定義網路
聯邦學習
上行分流器
多接取邊緣運算
服務品質
Network Function Virtualization
Software Defined Networking
Federated Learning
Uplink Classifier
Multi-access Edge Computing
Quality of Service |
| Date: | 2024 |
| Issue Date: | 2024-10-04 10:47:35 (UTC+8) |
| Abstract: | 隨著5G網路的快速發展，網路流量和用戶數也呈現爆炸性的增長，許多新型應用也應運而生，在不同場景下，用戶對網路的需求各不相同，但主要都離不開三大類：增強型行動寬頻（enhanced mobile broadband, eMBB），提供更大頻寬容量。極低延遲的可靠通訊（ultra-reliable and low latency communications, uRLLC），提供小於1毫秒及更可靠的通訊。巨量物聯網通訊（massive machine type communications, mMTC），提高連線數，滿足每平方公里最少有一百萬的連線裝置數；最終提升用戶使用5G網路時的服務品質(QoS, Quality of Service)。
在本研究中，我們模擬多個UE (User Equipment, 即使用設備)在固定數量的基地台之間移動，在網路功能虛擬化(Network Function Virtualization，NFV)與軟體定義網路(Software Defined Networking，SDN)的基礎上，結合聯邦學習技術，預測下一個時間點可能有最多UE的基地台，由會話管理功能網元(SMF)指派該區域的用戶平面功能網元(UPF)為上行分流器(Uplink Classifier, ULCL)，針對UE送到基地台的流量進一步分類，根據延遲與頻寬需求引導至不同的錨點(Anchor UPF)，最後送往數據網路，當使用者需要較即時性的反應，如視訊直播、車聯網，將流量引導至較近的Edge UPF，由多接取邊緣運算(Multi-access Edge Computing, MEC)裝置就近處理，減少大量數據在回程(Backhaul)網路上傳輸，不但減少延遲，也解決不同營運商在核心網路中因為數據隱私而造成無法有效整合的問題。
With the rapid development of 5G networks, network traffic and user numbers have experienced explosive growth, and many new applications have emerged. In different scenarios, users have varying network requirements, which can be broadly classified into three main categories: enhanced Mobile Broadband (eMBB), providing larger bandwidth capacity; Ultra-Reliable and Low Latency Communications (uRLLC), offering sub-millisecond and more reliable communication; and massive Machine Type Communications (mMTC), increasing the number of connections to support at least one million connected devices per square kilometer. The ultimate goal is to enhance the Quality of Service (QoS) for users when utilizing 5G networks.
In this research, we simulate multiple User Equipment (UE) moving between a fixed number of base stations. Building upon the foundations of Network Function Virtualization (NFV) and Software Defined Networking (SDN), we integrate federated learning techniques to predict the base station that may have the most UE at the next time point. The Session Management Function (SMF) designates the User Plane Function (UPF) in that area as the Uplink Classifier (ULCL) to further classify the traffic sent by UE to the base station. Based on latency and bandwidth requirements, the traffic is guided to different Anchor UPFs and finally sent to the data network. When users require more real-time responses, such as video streaming or vehicle-to-everything (V2X) communication, the traffic is directed to a closer Edge UPF, where it is processed by nearby Multi-access Edge Computing (MEC) devices. This approach not only reduces latency by minimizing the transmission of large amounts of data over the backhaul network but also addresses the issue of ineffective integration between different operators in the core network due to data privacy concerns. |
| Reference: | [1] "The 5G Core Network Demystified"[Online]. Available:
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[2] Devaki, C., Rainer, L.& Juho, P.(2019) “5G for the Connected World. ” John Wiley & Sons Ltd.
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[4] Tze-Jie, T., Fu-Lian, W., Wei-Ting, H., Jyh-Cheng, C., Cheng-Ying, H. (September 2020). ”A Reliable Intelligent Routing Mechanism in 5G Core Networks” In MobiCom '20: Proceedings of the 26th Annual International Conference on Mobile Computing and Networking.
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[16] “edge-computing”, [Online]. Available: https://www.3gpp.org/technologies/edge-computing |
| Description: | 碩士
國立政治大學
資訊科學系
111753164 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0111753164 |
| Data Type: | thesis |
| Appears in Collections: | [資訊科學系] 學位論文
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