Digital-Twin Enabled Time Ahead Resource Allocation for Integrated Fiber-Wireless Connected Vehicular Network
Bibliographic record
Abstract
The digital twin (DT) is envisaged as a catalyst for pioneering ecosystems of service provision within an immersive environment born from the convergence of virtual and physical realms. Specifically, DT could enhance the performance of edge-intelligent connected vehicular networks by allocating network resources efficiently based on the key performance indicators (KPIs) of vehicular data traffic. Consequently, this work addresses the key challenge of computation and spectrum resource allocation for vehicular networks. To allocate the optimal resource allocation, we subdivided the problem into: traffic classification, collective learning, and resource allocation scheme. In order to do so, this paper concentrates on two crucial vehicular applications: brake application and lane-change application. We utilize a random forest model to collectively learn vehicular data traffic in the upcoming time slot. Thereafter, a time-ahead resource allocation algorithm is proposed to improve the quality of service (QoS) by intelligently offloading vehicular data traffic to a DT-based integrated fiber-wireless (Fi-Wi) connected vehicular network. We evaluate the performance of the resource allocation strategy in terms of resources required by the network alongside the packet loss rate. It was observed that there was a 44.74% increase in cost as the total computation resources increased from F = 50 to 100 GHz, whereas the PLR of the network decreased by 71.43%.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".