MétaCan
Menu
Back to cohort
Record W4398152225 · doi:10.1109/access.2024.3403486

Analyzing the Impact of TXOP Allocation on Legacy Devices in IEEE 802.11bd Networks

2024· article· en· W4398152225 on OpenAlexafffund
Farzaneh Abdolahi, Jelena Mišić, Vojislav B. Mišić

Bibliographic record

VenueIEEE Access · 2024
Typearticle
Languageen
FieldEngineering
TopicVehicular Ad Hoc Networks (VANETs)
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer networkComputer scienceMarkov chainQueueing theoryThroughputIEEE 802Wireless lanChannel (broadcasting)IEEE 802.11pIEEE 802.11e-2005Transmission (telecommunications)IEEE 802.11Markov processWirelessQuality of serviceVehicular ad hoc networkWireless networkWireless ad hoc networkTelecommunicationsStatistics

Abstract

fetched live from OpenAlex

This paper presents a comprehensive analysis of V2X communication, focusing on the transition from IEEE 802.11p to IEEE 802.11bd standards, and introduces an analytical framework combining M/G/1 queuing analysis and Markov chain analysis. Our model includes saturation and non-saturation states of Enhanced Distributed Channel Access (EDCA) in the scenarios with and without fallback mechanism for IEEE 802.11bd Next Generation Vehicles (NGV) and focuses on optimizing Transmission Opportunity (TXOP) allocation for legacy (non-NGV) devices whilst taking into account the impact of vehicle-to-roadside Unit (RSU) distance. Our results show increased throughput for higher-priority traffic classes with augmented TXOP allocation for NGV devices and highlight the need to align RSU transmission coverage with On-Board Unit (OBU) data patterns and vehicle distance to prevent network saturation.

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)0.003
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: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.019
GPT teacher head0.304
Teacher spread0.286 · 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

Citations4
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueIEEE AccessSame topicVehicular Ad Hoc Networks (VANETs)French-language works237,207