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Record W2079523679 · doi:10.1109/wcnc.2014.6952082

Performance of two-way multi-relay inter-vehicular cooperative networks

2014· article· en· W2079523679 on OpenAlexaff
Reza Shakeri, Hamidreza Khakzad, Abbas Taherpour, Saeed Gazor

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsQueen's University
Fundersnot available
KeywordsRelayComputer scienceRayleigh fadingChannel (broadcasting)WirelessCooperative diversityComputer networkFadingUpper and lower boundsThroughputPhase-shift keyingWireless networkCommunications systemChannel state informationTelecommunicationsBit error rateMathematics

Abstract

fetched live from OpenAlex

Vehicle-to-vehicle (V2V) communications have recently received growing attentions due to the fact that it enables dynamic wireless data exchange between nearby vehicles, offering the opportunity for safety improvements and information sharing. In this paper, we investigate the performance analysis of a two-way amplify-and-forward (AF) multi-relay cooperative V2V communication networks with MPSK modulation, for two cases where the relays are vehicles or they are access-points of the road. In the mobile-to-mobile communication system considered in this paper, the communication channel is modeled as double-Rayleigh fading channel. We derive the exact symbol error probability (SEP) expressions under the two-phase and three-phase scenarios followed by their upper bound expressions. Then we examine the effect of the relays' position with respect to the source terminals, on their SEP and lastly, the maximum achievable diversity order under each scenario is studied.

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)0.001
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 score0.899
Threshold uncertainty score0.470

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.028
GPT teacher head0.272
Teacher spread0.244 · 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

Citations12
Published2014
Admission routes1
Has abstractyes

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