AI-Enabled Digital Twins for Next-Generation Networks: Forecasting Traffic and Resource Management in 5G/6G
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866908751629385728 |
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| author | Sengendo, John Granelli, Fabrizio |
| author_facet | Sengendo, John Granelli, Fabrizio |
| contents | As 5G and future 6G mobile networks become increasingly more sophisticated, the requirements for agility, scalability, resilience, and precision in real-time service provisioning cannot be met using traditional and heuristic-based resource management techniques, just like any advancing technology. With the aim of overcoming such limitations, network operators are foreseeing Digital Twins (DTs) as key enablers, which are designed as dynamic and virtual replicas of network infrastructure, allowing operators to model, analyze, and optimize various operations without any risk of affecting the live network. However, for Digital Twin Networks (DTNs) to meet the challenges faced by operators especially in line with resource management, a driving engine is needed. In this paper, an AI (Artificial Intelligence)-driven approach is presented by integrating a Long Short-Term Memory (LSTM) neural network into the DT framework, aimed at forecasting network traffic patterns and proactively managing resource allocation. Through analytical experiments, the AI-Enabled DT framework demonstrates superior performance benchmarked against baseline methods. Our study concludes that embedding AI capabilities within DTs paves the way for fully autonomous, adaptive, and high-performance network management in future mobile networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_20796 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI-Enabled Digital Twins for Next-Generation Networks: Forecasting Traffic and Resource Management in 5G/6G Sengendo, John Granelli, Fabrizio Networking and Internet Architecture As 5G and future 6G mobile networks become increasingly more sophisticated, the requirements for agility, scalability, resilience, and precision in real-time service provisioning cannot be met using traditional and heuristic-based resource management techniques, just like any advancing technology. With the aim of overcoming such limitations, network operators are foreseeing Digital Twins (DTs) as key enablers, which are designed as dynamic and virtual replicas of network infrastructure, allowing operators to model, analyze, and optimize various operations without any risk of affecting the live network. However, for Digital Twin Networks (DTNs) to meet the challenges faced by operators especially in line with resource management, a driving engine is needed. In this paper, an AI (Artificial Intelligence)-driven approach is presented by integrating a Long Short-Term Memory (LSTM) neural network into the DT framework, aimed at forecasting network traffic patterns and proactively managing resource allocation. Through analytical experiments, the AI-Enabled DT framework demonstrates superior performance benchmarked against baseline methods. Our study concludes that embedding AI capabilities within DTs paves the way for fully autonomous, adaptive, and high-performance network management in future mobile networks. |
| title | AI-Enabled Digital Twins for Next-Generation Networks: Forecasting Traffic and Resource Management in 5G/6G |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2510.20796 |