Digital Twin-empowered Network Slicing in B5G Networks: Experience-driven approach
Bibliographic record
Abstract
Network slicing is considered a promising networking pillar of efficient resource management in beyond 5G (B5G) networks. However, the dynamic and complex characteristics of future networks pose challenges in designing novel resource allocation techniques due to the stringent quality of service (QoS) requirements and virtualized network infrastructures. To solve this issue, we propose a digital twin (DT)-enabled deep distributional Q-network (DDQN) framework that constructs a digital mirror of the physical slicing-enabled network to simulate its complex environment and predict the dynamic characteristics of the network. The DT of network slicing is expressed as a graph, and a graph neural network (GNN) is developed to learn the complicated relationships of the network slice. The graph-based network states are forwarded to the DDQN agent to learn the optimal network slicing policy. Through simulations, it is demonstrated that the proposed technique can satisfy the stringent QoS requirements and achieve near-optimal performance in a dynamic B5G network.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".