| 摘要(中) |
近年O-RAN聯盟提出的開放式無線電接入網路(Open Radio Access Network, O-RAN)已成為電信網路商在部署基地台方面著眼的新標準，O-RAN與傳統的基地台架構不同，它把原本的架構分解了無線電單元(Radio Unit, RU)、分佈式單元(Distributed Unit, DU)以及集中單元(Central Unit, CU)，行動網路運營商(Mobile Network Operator, MNO)可以安裝自己的無線電單元，並租賃可以計算他們所需的分佈式單元(DU)和集中單元(CU)功能的計算資源，使用這種方式，便形成了一個多租戶架構的O-RAN網路。
隨著5G網路的制定及商轉，行動裝置的要求變得複雜多變，根據5G其中的兩大應用情境URLLC(Ultra-Reliable and Low Latency Communication)以及eMBB(Enhanced Mobile Broadband)，裝置可能會提出低延遲及高可靠度傳輸，或是高頻寬的要求，以應對不同的使用情境。
本篇論文提出一個基於5G O-RAN多租戶網路的系統，針對URLLC和eMBB任務，將連接Macro cell RU的Edge Cloud以及位於中心機房的Regional Cloud 視為邊緣運算架構，使用集中式的控制器來決定任務卸載策略，本篇論文所提出的方法可以改善整個系統中行動網路運營商的營業成本，降低任務拒絕率及提高資料吞吐量。 |
| 摘要(英) |
In recent years, the Open Radio Access Network (O-RAN), proposed by the O-RAN Alliance, has become a new standard focused on deploying base stations for telecommunication network operators. Unlike traditional base station architectures, O-RAN decomposes the original structure into Radio Unit (RU), Distributed Unit (DU), and Central Unit (CU). Mobile network operators can install their own radio units and lease computational resources for DUs and CUs, forming a multi-tenant architecture O-RAN network.
With the establishment and commercialization of 5G networks, the requirements for mobile devices have become increasingly complex. Based on the two major 5G application scenarios, Ultra-Reliable and Low Latency Communication (URLLC) and Enhanced Mobile Broadband (eMBB), devices may demand low latency and high reliability transmission or high bandwidth to cater to different usage scenarios.
This paper proposes a system based on a 5G O-RAN multi-tenant network, focusing on URLLC and eMBB tasks. It considers the Edge Cloud connecting Macro cell RUs and the Regional Cloud located in the central data center as edge computing infrastructures, utilizing a centralized controller to determine task offloading strategies. The proposed method can improve the operational costs of mobile network operators, reduce task rejection rates, and enhance data throughput in the system. |
| 參考文獻 |
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[11] ETSI, "Multi-access Edge Computing (MEC) Framework and Reference Architecture," in ETSI GS MEC 003 V3.1.1, 2022-03.
[12] Sourav Mondal and Marco Ruffini, "A Min-Max Fair Resource Allocation Framework for Optical x-haul and DU/CU in Multi-tenant O-RANs," in ICC 2022 - IEEE International Conference on Communications, Seoul, Korea, 2022.
[13] Anselme Ndikumana, Kim Khoa Nguyen and Mohamed Cheriet, “Age of Processing-Based Data Offloading for Autonomous Vehicles in MultiRATs Open RAN,” IEEE Transactions on Intelligent Transportation Systems ( Volume: 23, Issue: 11, November 2022) , pp. 21450 - 21464, 25 7 2022.
[14] Anselme Ndikumana, Kim Khoa Nguyen and Mohamed Cheriet, “Federated Learning Assisted Deep Q-Learning for Joint Task Offloading and Fronthaul Segment Routing in Open RAN,” IEEE Transactions on Network and Service Management ( Early Access ), pp. 1 - 1, 15 2 2023.
[15] Cheng Cheng Zhang, Kim Khoa Nguyen and Mohamed Cheriet, "Routing and Packet Scheduling For Virtualized Disaggregate Functions in 5G O-RAN Fronthaul," in 2021 IEEE Global Communications Conference (GLOBECOM), Madrid, Spain, 2021.
[16] Zeinab Akhavan, Mona Esmaeili, Babak Badnavay, Mohammad Yousefi, Xiang Sun, Michael Devetsikiotis and Payman Zarkesh-Ha, "Deep Reinforcement Learning for Online Latency Aware Workload Offloading in Mobile Edge Computing," in 2022 IEEE Global Communications Conference (GLOBECOM), Rio de Janeiro, Brazil, 2022. |
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