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Record W4386702634 · doi:10.1109/lcomm.2023.3315010

InP-CRS: An Intra-Platoon Cooperative Resource Selection Scheme for C-V2X Networks

2023· article· en· W4386702634 on OpenAlexafffund
Bingying Wang, Jun Zheng, Nathalie Mitton, Cheng Li

Bibliographic record

VenueIEEE Communications Letters · 2023
Typearticle
Languageen
FieldEngineering
TopicVehicular Ad Hoc Networks (VANETs)
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaFundamental Research Funds for the Central UniversitiesSimon Fraser University
KeywordsPlatoonComputer scienceScheme (mathematics)Computer networkScheduling (production processes)Network packetSelection (genetic algorithm)Resource (disambiguation)Distributed computingReal-time computingEngineeringArtificial intelligenceControl (management)Mathematics

Abstract

fetched live from OpenAlex

This letter proposes an Intra-Platoon Cooperative Resource Selection (InP-CRS) scheme for frequency-time resource selection to support reliable intra-platoon message delivery. The proposed InP-CRS scheme is based on the standardized sensing-based semi-persistent scheduling (SPS) scheme designed for C-V2X mode 4 and is intended to address the hidden-node problem in intra-platoon communication. Specifically, the InP-CRS scheme introduces an intra-platoon cooperative mechanism, in which the last member of a platoon needs to report the frequency-time resource occupation information of the platoon’s hidden nodes to the platoon leader (PL) so that the PL can exclude those resources occupied by the hidden nodes of the platoon during its resource selection. In this way, potential packet collisions caused by the hidden nodes of a platoon can be largely reduced. Simulation results show that the proposed InP-CRS scheme can effectively improve the reliability of intra-platoon message delivery in terms of the successful broadcast probability of a PL as compared to the standardized sensing-based SPS scheme.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.338
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.026
GPT teacher head0.263
Teacher spread0.237 · 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 teacher head, not a consensus.

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

Citations7
Published2023
Admission routes2
Has abstractyes

Explore more

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