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Full Duplex of V2V Cooperative Relaying over Cascaded Nakagami-m Fading Channels

2020· article· en· W3117177331 on OpenAlex

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affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFull-Duplex Wireless Communications
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsNakagami distributionFadingRelayCumulative distribution functionProbability density functionTopology (electrical circuits)Independent and identically distributed random variablesComputer scienceChannel state informationSignal-to-noise ratio (imaging)Interference (communication)Relay channelUpper and lower boundsMonte Carlo methodContext (archaeology)MathematicsChannel (broadcasting)TelecommunicationsWirelessStatisticsRandom variablePhysicsCombinatoricsMathematical analysis

Abstract

fetched live from OpenAlex

In this paper, we consider full duplex amplify and forward (AF) relay networks in vehicle-to-vehicle (V2V) applications. In this context, taking into account the self-interference (SI) at the relay and assuming independent and not necessarily identically distributed (i.n.i.d) generalized L-Nakagami-m fading channels, we derive novel expressions for the probability density function (PDF) and cumulative distribution function (CDF) of the signal-to-interference-plus-noise ratio at the relay. Capitalizing on this, a lower bound to the end-to-end outage probability is derived. Monte-Carlo simulation results are presented to corroborate the derived analytical results. Our results show that the channel cascading significantly impacts the end-to-end outage probability of full duplex (FD) relaying V2V systems, where the cascading of the source-relay link is shown to be more significant than that of the relay-destination link.

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.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
Threshold uncertainty score0.741

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.031
GPT teacher head0.238
Teacher spread0.207 · 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

Quick stats

Citations9
Published2020
Admission routes1
Has abstractyes

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