Conflict-Free Scheduling in Cellular V2X Communications
Bibliographic record
Abstract
Cellular V2X, the “Vehicle to Everything” standard, defines a framework for information exchange among vehicles and other network entities. In one of the main modes, LTE V2X relies on a central scheduler to minimize the consumed resources in a conflict-free manner. This NP-hard problem leads to a scheduling strategy that assigns separate resources to the conflicting links and can enjoy from opportunistic resource reuse. In this paper, a novel polynomial-time heuristic, MUCS, is introduced, which models this scheduling as a Vehicle Routing Problem. The existing conflict-free schedulers face major incompetence in satisfying the LTE and 5G V2X requirements mainly due to their reliance on simplifications that are unnatural to the V2X environment. On the contrary, MUCS can flexibly accommodate general Device-to-Device topologies as the basis of V2X networks without imposing any packet segmentation. This way, MUCS minimizes the control information overhead in the cellular V2X standard. Due to scalability and a high quality resource utilization (near-optimal in certain conditions), compared to the existing literature, MUCS is desirable for LTE V2X and the 5G networks.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".