Two-Level Soft RAN Slicing for Customized Services in 5G-and-Beyond Wireless Communications
Bibliographic record
Abstract
In this article, a two-level soft-slicing scheme is proposed for 5G-and-beyond radio access networks to support ultrareliable and low-latency communications (URLLC) and enhanced mobile broadband (eMBB) services with delay/reliability and throughput requirements, respectively. At the network level, we first determine the number of radio resources required for eMBB services and analyze the delay violation probability for URLLC services. Then, an integer nonlinear program is formulated for the network-level resource preallocation. Since the formulated problem is NP-complete, a low-complexity heuristic algorithm is proposed to obtain near-optimal solutions. Given the preallocated resources at each gNodeB (gNB), a gNB-level resource scheduling scheme is designed to enable real-time resource sharing among URLLC services considering the reliability and delay requirements. Simulation results show that the proposed soft-slicing scheme meets stringent quality-of-service requirements for both URLLC and eMBB services and achieves high resource utilization efficiency when compared with conventional hard resource slicing schemes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".