Retraction Notice: Softwarized Resource Management and Allocation With Autonomous Awareness for 6G-Enabled Cooperative Intelligent Transportation Systems
Bibliographic record
Abstract
Cooperative intelligent transport system (C-ITS) is one emerging application scenario in 6G. Within the content of 6G, softwarization is the dominant attribute of networks. 6G networks are required to have the intelligence and autonomy attributes, too. With softwarization and autonomy, not only the network capable of flexibly managing softwarized resources can be achieved, but also the network can learn and adapt itself with respect to the dynamic networking environment. However, multiple issues stand in the way of developing 6G networks, requiring to be addressed. In this paper, the softwarized resource management and allocation with autonomy and intelligence awareness in 6G networks for C-ITS application is researched. Firstly, key enabling technologies and problem model of 6G-enabled C-ITS are described. Then, an architecture design enabling to achieve the intelligent and softwarized resource management and allocation per service request, abbreviated as ReMaAl-AutoNet, is proposed. The proposed architecture design, based on reinforcement learning (RL), can realize the intelligent resource management and allocation by undergoing the training. Afterwards, simulations are illustrated to validate the proposed ReMaAl-AutoNet architecture. For instance, the successful ratio of ReMaAl-AutoNet has an advantage of over ten percentages than the direct counterpart without training.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Research integrity Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| gpt | Research integrity Domain: not available · Genre: Editorial About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.011 | 0.020 |
| Insufficient payload (model declined to judge) | 0.014 | 0.011 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".