Digital Twin Assisted Closed-Loops for Energy-Efficient Open RAN-Based Fixed Wireless Access Provisioning in Rural Areas
Bibliographic record
Abstract
For digital inclusion, Internet quality in Low-Density and Rural Areas (LDRAs) should be enhanced to satisfy QoS requirements of various services and applications. Due to the high operating costs of fiber optic deployment in LDRAs, 5G Fixed Wireless Access (5G FWA) is becoming a more attractive solution. Furthermore, 5G services require edge cloud deployment for proximity computation, which increases both required network and energy resources. Therefore, we propose closed-loops assisted by Digital Twin (DT) for energy-efficient Open RAN-based FWA provisioning in LDRAs. We consider a 5G FWA and edge cloud system as Physical Twin (PT) and design a closed-loop that distributes radio resources to edge cloud instances that manage network slices for scheduling purposes. We propose another closed-loop for intra-slice resource allocation to LDRAs. We develop an energy model and join radio resource allocation with the energy model. Then, we design reinforcement learning and optimization approaches to maximize delay requirement satisfaction while minimizing energy cost. Finally, we present DT replicating PT by incorporating solution experiences into future states. The results show that our approach uses energy resources efficiently while satisfying delay requirements of slices.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".