User-Centric Slice Allocation Scheme in 5G Networks and Beyond
Why this work is in the frame
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Bibliographic record
Abstract
Network slicing is a key enabler in the Next Generation 5G Radio Access Network (RAN) to build the RAN-as-a-Service concept. Cloud-RAN, Network Function Virtualization, Software Defined Network and RAN functional splits are the main pillars expected to be integrated to provide the required flexibility. One of the major concerns is to efficiently allocate RAN resources for slices, while supporting multiple use-cases with heterogeneous Quality-of-Service (QoS) requirements. Current related work is adopting radio resource allocation scheme by considering a cell-centric deployment approach for slice embedding. However, to achieve greater flexibility and fine-grained tunable resource utilization, we believe that the deployment scheme should be integrated in the slice design. In this paper, we go a step further and propose a RAN slicing approach with customized deployment scheme on user basis. As the corresponding optimization problem is NP-Hard, we propose a low-cost and efficient heuristic algorithm for RAN Slice allocation based on the Particle Swarm Optimization approach. Our proposal jointly harnesses radio, processing and link resources at user level tailored to the QoS requirements, while customizing efficiently the underlying physical RAN resource usage.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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