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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.19961 |
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| _version_ | 1866911284819132416 |
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| author | Tao, Zhenyu Xu, Wei You, Xiaohu |
| author_facet | Tao, Zhenyu Xu, Wei You, Xiaohu |
| contents | Optimizing modern wireless networks is exceptionally challenging due to their high dynamism and complexity. While the agentic artificial intelligence (AI) powered by reinforcement learning (RL) offers a promising solution, its practical application is limited by prohibitive exploration costs and potential risks in the real world. The emerging digital twin (DT) technology provides a safe and controlled virtual environment for agentic AI training, but its effectiveness critically depends on the DT's fidelity. Policies trained in a low-fidelity DT that does not accurately represent the physical network may experience severe performance degradation upon real-world deployment. In this article, we introduce a unified DT evaluation framework to ensure trustworthy DTs in agentic AI-based network optimization. This evaluation framework shifts from conventional isolated physical accuracy metrics, such as wireless channel and user trajectory similarities, to a more holistic, task-centric DT assessment. We demonstrate it as an effective guideline for design, selection, and lifecycle management of wireless network DTs. A comprehensive case study on a real-world wireless network testbed shows how this evaluation framework is used to pre-filter candidate DTs, leading to a significant reduction in training and testing costs without sacrificing deployment performance. Finally, potential research opportunities are discussed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_19961 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Toward Trustworthy Digital Twins in Agentic AI-based Wireless Network Optimization: Challenges, Solutions, and Opportunities Tao, Zhenyu Xu, Wei You, Xiaohu Systems and Control Optimizing modern wireless networks is exceptionally challenging due to their high dynamism and complexity. While the agentic artificial intelligence (AI) powered by reinforcement learning (RL) offers a promising solution, its practical application is limited by prohibitive exploration costs and potential risks in the real world. The emerging digital twin (DT) technology provides a safe and controlled virtual environment for agentic AI training, but its effectiveness critically depends on the DT's fidelity. Policies trained in a low-fidelity DT that does not accurately represent the physical network may experience severe performance degradation upon real-world deployment. In this article, we introduce a unified DT evaluation framework to ensure trustworthy DTs in agentic AI-based network optimization. This evaluation framework shifts from conventional isolated physical accuracy metrics, such as wireless channel and user trajectory similarities, to a more holistic, task-centric DT assessment. We demonstrate it as an effective guideline for design, selection, and lifecycle management of wireless network DTs. A comprehensive case study on a real-world wireless network testbed shows how this evaluation framework is used to pre-filter candidate DTs, leading to a significant reduction in training and testing costs without sacrificing deployment performance. Finally, potential research opportunities are discussed. |
| title | Toward Trustworthy Digital Twins in Agentic AI-based Wireless Network Optimization: Challenges, Solutions, and Opportunities |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2511.19961 |