A Digital Twin Based Learning Architecture for Resource Allocation in O-RAN

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林彥澄(Yen-Chen Lin) 查詢紙本館藏

畢業系所

通訊工程學系

論文名稱

(A Digital Twin Based Learning Architecture for Resource Allocation in O-RAN)

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至系統瀏覽論文 (2026-8-31以後開放)

摘要(中)

隨著網路環境的持續發展，當前網路性能已不能滿足期望，需要進一步改進。O-RAN 引入 AI/ML workflow，旨在實現網路優化、預測性維護、智能流量管理、安全檢測和服務保證。將 AI/ML 整合到 O-RAN 中，運營商可以創建更高效、可靠和智能的網路，提供更優質的服務，降低成本，並適應不斷變化的需求。為解決對 AI/ML 模型信任度的問題，可採取包括選擇透明度高的模型、進行嚴格測試、遵守標準規格，以及進行跨領域協作。在虛擬環境中部署智能控制方法，如 near-RT RIC 的 xApp，可驗證演算法可行性並最小化錯誤決策。通過結合 AI/ML 技術的培訓環境，運營商能夠建立自動化的應用開發流程，同時降低與 AI/ML 部署相關的風險，從而實現網路性能的全面提升。

摘要(英)

With the continuous development of the network environment, the current net-work performance has fallen short of expectations and there is a need for further improvement. Hence, O-RAN introduces AI/ML workflows to achieve network optimization, predictive maintenance, intelligent traffic management, security and anomaly detection, and customer experience. By integrating AI/ML workflows into O-RAN, operators can leverage the power of data-driven decision-making, automation, and optimization to create more efficient, reliable, and intelligent mobile networks. This enables them to deliver better service quality, reduce costs, and adapt to the evolving needs of mobile communication systems. To address concerns regarding trust in AI/ML models where network operators have limited control, several measures can be taken. Firstly, selecting models that offer transparency and explainability ensures operators can understand and interpret the decision-making process. Additionally, rigorous testing and validation in various scenarios, including simulated and real-world environments, help evaluate performance and reliability. Adhering to industry standards, regulations, and implementing security measures and privacy compliance also fosters trust. Collaborative development and peer reviews involving experts from different domains provide external validation. When comparing AI/ML solutions, considering vendor reputation, track record, performance metrics, and
customer reviews is crucial. Lastly, to ensure real network performance, deploying intelligent control methods in virtual environments, such as near-RT RIC’s xApp, can verify algorithm feasibility and minimize erroneous decisions. By leveraging training environments that combine expert knowledge with AI/ML techniques, operators can establish fully automated app development processes and mitigate risks associated with AI/ML deployments.

關鍵字(中)

★ 開放式無線網路
★ 排程演算法
★ 無線基地台

關鍵字(英)

★ Open RAN
★ Celluar
★ 6G
★ Scheduling
★ ns-3

論文目次

1 Introduction 1
2 Related Works 5
2.1 O-RAN Intelligence in the RIC 5
2.2 DT based ns-3 environment 6
2.3 Implementing risk for telecommunication operators 7
3 ns-3-based DT-Integrated Framework for Network Optimization 9
3.1 A DT-Integrated Approach 9
3.2 Key Features and Advantages 9
4 Problem Formulation and MDP Model 11
4.1 Problem Formulation 11
4.2 MDP model 12
4.2.1 Dynamic Algorithm Selection Model for O-RAN Near-RT RIC 13
4.2.2 Componentized Action 14
5 Scheduler Architecture and Implementation 17
5.1 Integration with ns-3 17
5.2 Component Architecture Design 18
5.3 Workflow and Decision Process 19
6 Numerical Results 21
6.1 Simulation Setup 21
6.2 Performance Evaluation 24
7 Conclusions 28
Bibliography 32

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指導教授

黃志煒(Chih-Wei Huang)

審核日期

2024-8-15

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