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Record W2345267774 · doi:10.1109/tmc.2016.2519343

Relay-Assisted Device-to-Device Communication: A Stochastic Analysis of Energy Saving

2016· article· en· W2345267774 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

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

Bibliographic record

VenueIEEE Transactions on Mobile Computing · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced MIMO Systems Optimization
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsRelayComputer scienceEnergy consumptionPoisson point processEnergy (signal processing)Monte Carlo methodWirelessProbabilistic logicPoint (geometry)Stochastic geometryEfficient energy usePoint processElectronic engineeringTelecommunicationsElectrical engineeringMathematicsEngineeringStatisticsArtificial intelligence

Abstract

fetched live from OpenAlex

This paper lays a mathematical framework for estimating the energy saving of a relay assisting a pair of wireless devices. We derive closed-form expressions for describing the geometrical zone where relaying is energy efficient. In addition, we obtain the probabilistic distribution of the energy saving introduced by relays that are randomly distributed according to a spatial Poisson point process. Furthermore, we present a comparison methodology for fairly evaluating the energy consumption of conventional cellular network from one side and relay-assisted device-to-device communication from another side. Results suggest that a significant energy saving can be achieved when relay-assisted device-to-device communication is adopted for distances below a certain threshold. In order to test the analytical framework, we perform Monte-Carlo simulations and compare the results with those obtained from the mathematical framework.

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: none
Teacher disagreement score0.943
Threshold uncertainty score0.759

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.250
Teacher spread0.236 · 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