Digital Twin Network-Based 6G Self-Evolution
Digital twins (DTs) will revolutionize network autonomy. Recent studies have promoted the idea of a DT-native 6G network, deeply integrating DTs into mobile network architectures to improve the timeliness of physical–digital synchronization and network optimizations. However, DTs have mainly acted j...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/11/3543 |
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| author | Yuhong Huang Mancong Kang Yanhong Zhu Na Li Guangyi Liu Qixing Wang |
| author_facet | Yuhong Huang Mancong Kang Yanhong Zhu Na Li Guangyi Liu Qixing Wang |
| author_sort | Yuhong Huang |
| collection | DOAJ |
| description | Digital twins (DTs) will revolutionize network autonomy. Recent studies have promoted the idea of a DT-native 6G network, deeply integrating DTs into mobile network architectures to improve the timeliness of physical–digital synchronization and network optimizations. However, DTs have mainly acted just as a tool for network autonomy, leading to a gap regarding the ultimate goal of network self-evolution. This paper analyzes future directions concerning DT-native networks. Specifically, the proposed architecture introduces a key concept called “future shots” that gives accurate network predictions under different time scales of self-evolution strategies for various network elements. To realize the future shots, we propose a long-term hierarchical convolutional graph attention model for cost-effective network predictions, a conditional hierarchical graph neural network for strategy generation, and methods for efficient small-to-large-scale interactions. The architecture is expected to facilitate high-level network autonomy for 6G networks. |
| format | Article |
| id | doaj-art-70ec4ae4ded145c09a0f5823a25342d1 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-70ec4ae4ded145c09a0f5823a25342d12025-08-20T02:22:59ZengMDPI AGSensors1424-82202025-06-012511354310.3390/s25113543Digital Twin Network-Based 6G Self-EvolutionYuhong Huang0Mancong Kang1Yanhong Zhu2Na Li3Guangyi Liu4Qixing Wang5China Mobile Research Institute, Beijing 100053, ChinaChina Mobile Research Institute, Beijing 100053, ChinaChina Mobile Research Institute, Beijing 100053, ChinaChina Mobile Research Institute, Beijing 100053, ChinaChina Mobile Research Institute, Beijing 100053, ChinaChina Mobile Research Institute, Beijing 100053, ChinaDigital twins (DTs) will revolutionize network autonomy. Recent studies have promoted the idea of a DT-native 6G network, deeply integrating DTs into mobile network architectures to improve the timeliness of physical–digital synchronization and network optimizations. However, DTs have mainly acted just as a tool for network autonomy, leading to a gap regarding the ultimate goal of network self-evolution. This paper analyzes future directions concerning DT-native networks. Specifically, the proposed architecture introduces a key concept called “future shots” that gives accurate network predictions under different time scales of self-evolution strategies for various network elements. To realize the future shots, we propose a long-term hierarchical convolutional graph attention model for cost-effective network predictions, a conditional hierarchical graph neural network for strategy generation, and methods for efficient small-to-large-scale interactions. The architecture is expected to facilitate high-level network autonomy for 6G networks.https://www.mdpi.com/1424-8220/25/11/35436Gdigital twin networkpre-validation environmentgraph neural network |
| spellingShingle | Yuhong Huang Mancong Kang Yanhong Zhu Na Li Guangyi Liu Qixing Wang Digital Twin Network-Based 6G Self-Evolution Sensors 6G digital twin network pre-validation environment graph neural network |
| title | Digital Twin Network-Based 6G Self-Evolution |
| title_full | Digital Twin Network-Based 6G Self-Evolution |
| title_fullStr | Digital Twin Network-Based 6G Self-Evolution |
| title_full_unstemmed | Digital Twin Network-Based 6G Self-Evolution |
| title_short | Digital Twin Network-Based 6G Self-Evolution |
| title_sort | digital twin network based 6g self evolution |
| topic | 6G digital twin network pre-validation environment graph neural network |
| url | https://www.mdpi.com/1424-8220/25/11/3543 |
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