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Record W2129871822 · doi:10.1109/ccece.2011.6030493

Cooperative inter-vehicular communications in highway traffic

2011· article· en· W2129871822 on OpenAlexaff
Mohamed F. Feteiha, Murat Uysal, Abdul Rahim Ahmad

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPairwise error probabilityRelayFadingComputer scienceChannel (broadcasting)Cooperative diversityComputer networkMonte Carlo methodAntenna diversityTopology (electrical circuits)WirelessTelecommunicationsMathematicsStatisticsEngineeringPower (physics)Electrical engineeringPhysics

Abstract

fetched live from OpenAlex

In this paper, we investigate the performance of a single relay assisted cooperative vehicular network in a highway traffic scenario. Source and relaying vehicles are assumed to be traveling in the same direction with similar speeds. This results in a relative velocity nearly equal to zero and leads to a frequency-flat and time-flat fading in source-to relay link. On the other hand, source-to-destination and relay to-destination links are modeled as doubly-selective fading. To handle spreading in time and frequency, we propose a precoded cooperative scheme to exploit delay and Doppler spreads to our advantage. Under the assumption of amplify-and-forward relaying with orthogonal cooperation protocol, we derive a pairwise error probability expression and demonstrate the achievable diversity gains. We further conduct Monte Carlo simulations to confirm the analytical derivations and present the error rate performance of the proposed scheme with imperfect channel estimation.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.905
Threshold uncertainty score0.454

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.000
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 head0.100
GPT teacher head0.298
Teacher spread0.199 · 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.

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

Citations6
Published2011
Admission routes1
Has abstractyes

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