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Record W2151738743 · doi:10.1109/twc.2007.05742

Cooperative Connectivity Models for Wireless Relay Networks

2007· article· en· W2151738743 on OpenAlexaff
John Boyer, D.D. Falconer, Halim Yanıkömeroğlu

Bibliographic record

VenueIEEE Transactions on Wireless Communications · 2007
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsCarleton University
Fundersnot available
KeywordsRelayComputer scienceCooperative diversityComputer networkWirelessChannel (broadcasting)Constraint (computer-aided design)Wireless networkInterference (communication)Antenna diversityDistributed computingTelecommunicationsMathematics

Abstract

fetched live from OpenAlex

This paper considers the ways that cooperating terminals can be connected to each other in wireless relay networks and the constraints imposed by the availability of different system resources. A framework is developed that exposes the relationship between constraints on available system resources and the achievable combinations of communication links between cooperating terminals. Cooperative connectivity models defined by the achievable combinations of links are derived, associated with their minimum cost constraint sets, and mapped to diversity techniques presented in the literature. The constraints considered are the available number of orthogonal relaying channels, the ability of terminals to diversity combine signals on a single common channel, the ability of terminals to diversity combine signals on orthogonal channels, the ability of terminals to transmit signals on multiple orthogonal channels, and the ability of terminals to cancel the effects of interhop interference.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.003
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.061
GPT teacher head0.307
Teacher spread0.246 · 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 source (direct Gemma or distilled Codex), 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

Citations29
Published2007
Admission routes1
Has abstractyes

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