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Record W2918572158 · doi:10.1109/acssc.2018.8645203

Delay-aware Conflict-free Scheduling for LTE-V, Sidelink 5G V2X Vehicular Communication, in Highways

2018· article· en· W2918572158 on OpenAlexaff
Zahra Naghsh, Shahrokh Valaee

Bibliographic record

Venue2018 52nd Asilomar Conference on Signals, Systems, and Computers · 2018
Typearticle
Languageen
FieldEngineering
TopicVehicular Ad Hoc Networks (VANETs)
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceScheduling (production processes)ReuseComputer networkVehicular ad hoc networkDistributed computingReal-time computingWirelessWireless ad hoc networkTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

V2X, “Vehicle-to-Everything” sidelink in 5G, facilitates communication between vehicles, pedestrians and cyclists while relying on a central scheduler in the main mode. In many applications, sidelinks in highways are formed based on the location of vehicles on the road. This defines an inherent ordered-tree structure to be used for delay-aware scheduling in LTE frame. Having these in mind, we propose the first two-dimensional expansion of a popular leading delay-aware scheduler in the literature. Our designed scheduler matches the V2X LTE multichannel environment and benefits from resource reuse while being capable of handling delay in sensitive vehicular safety applications. Compared to the existing competitors, delay control is the main feature of our scheduler. Having less or similar complexity order, this feature comes at the cost of less efficient resource reuse.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.035
GPT teacher head0.247
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2018
Admission routes1
Has abstractyes

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