A Hierarchical Soft RAN Slicing Framework for Differentiated Service Provisioning
Why this work is in the frame
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Bibliographic record
Abstract
Network slicing is a key technology to allow resource sharing among heterogeneous operators/ services, which achieves QoS isolation for service provisioning in future communication networks. In this article, a comprehensive hierarchical soft-slicing framework is proposed to enable software-defined radio access networks supporting differentiated services with diverse QoS requirements. The proposed framework consists of network-level slicing and gNodeB-level slicing. In the network level, radio RBs are pre-allocated to each gNodeB in a large time scale, while in the gNodeB level, the pre-allocated RBs are dynamically scheduled to the services in response to the small time scale (mini-slot-level) RB request variations. The proposed framework allows the accommodation of time-varying traffic loads of differentiated services over multiple gNodeBs, while enabling dynamic inter-gNodeB RB sharing to increase the resource multiplexing gain. A case study is presented to demonstrate the effectiveness of the proposed framework, followed by a discussion on open research issues.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it