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Record W2125790276 · doi:10.1109/tvt.2014.2310708

Energy-Aware Cooperation Strategy With Uncoordinated Group Relays for Delay-Sensitive Services

2014· article· en· W2125790276 on OpenAlexaff
A-Long Jin, Wei Song, Peijian Ju, Dizhi Zhou

Bibliographic record

VenueIEEE Transactions on Vehicular Technology · 2014
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsComputer scienceComputer networkQuality of serviceScalabilityFadingNetwork packetEnergy consumptionTransmission (telecommunications)Transmission delayWirelessProvisioningTimerBroadcasting (networking)Channel (broadcasting)TelecommunicationsEngineering

Abstract

fetched live from OpenAlex

Due to channel fading and user mobility in wireless networks, quality-of-service (QoS) provisioning for multimedia services requires great effort. It is even more challenging to support the fast-growing multimedia services in a green manner. As a promising technique, cooperative communications make use of the broadcasting nature of the wireless medium to facilitate data transmission, and it can achieve energy saving. To support the delay-sensitive multimedia services in an energy-efficient manner, we consider a new framework in this paper where multiple source-destination pairs share a group of relays with an energy constraint. We also propose an effective uncoordinated cooperation strategy, which is based on the backoff timer. The theoretical performance bounds of the proposed strategy are derived with respect to the collision probability and the transmission success probability. As shown in the numerical and simulation results, the proposed strategy outperforms a probability-based uncoordinated strategy in terms of average packet delay, delay outage probability, and energy consumption. Further, we investigate the scalability of our proposed strategy and find that it can be deployed in a large-scale network.

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 score0.978
Threshold uncertainty score0.806

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 integrity0.0000.000
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 head0.011
GPT teacher head0.228
Teacher spread0.216 · 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

Citations15
Published2014
Admission routes1
Has abstractyes

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