Resource Scheduling for eMBB and URLLC Multiplexing in NOMA-Based VANETs: A Dual Time-Scale Approach
Bibliographic record
Abstract
Enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) are two critical services in vehicular networks. However, the presence of both services creates a difficult resource allocation problem due to their heterogeneous requirements. To address the challenge of simultaneously providing eMBB and URLLC services in vehicular networks, we propose a resource allocation approach that maximizes eMBB rate while ensuring that both URLLC latency and reliability requirements are satisfied. Our approach utilizes non-orthogonal multiple access (NOMA) technology, where the resource for eMBB services is allocated by slot and the traffic of URLLC service is accommodated using mini-slots. To solve this dual time-scale problem, we employ a dual decomposition and sub-gradient method to solve the power allocation and resource block assignment of eMBB services, while the Vogel's approximation method (VAM) and modified distribution method (MODI) are proposed to solve the URLLC resource allocation problem. Additionally, we present two low-complexity heuristic algorithms for the URLLC sub-problem. Simulation results indicate that our proposed approach surpasses baseline methods in terms of both eMBB rate and fairness.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".