Collaborative Game Theory and Deep Learning Closed-Loop Automation In O-RAN 5G Network Slicing For Smart Grid Applications
Bibliographic record
Abstract
5G intends to use network slicing to support multiple vertical industries such as the power grid. 5G network slicing can provide different levels of physical resources and virtual resources for various applications/services in vertical domains to meet their diversified communication requirements. These heterogeneous Service Level Agreements (SLAs) make the network highly dynamic in nature and challenging to operate and manage efficiently. In this paper, we formulate the SLA-based closed-loop automation network slicing management problem for 5G smart grid services in Open Radio Access Network (O-RAN). The resource scheduling problem is non-convex combinatorial while the resource reservation is a long-term mean-square-error minimization which is difficult to solve. We propose a collaborative game theory and deep learning solution that overcomes the complexity difficulty of the formulated problems. The proposed network slicing mechanism comprises three closed-loop control: closed-loop 1 resource request at the service layer, closed-loop 2 resource scheduling at the radio access layer, and closed-loop 3 resource reservation at the network layer. Simulation results show that the proposed slicing framework is more efficient than the baselines regarding fairness and network throughput.
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
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".