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Record W2154740911 · doi:10.1049/iet-com.2012.0739

Survey on cooperative medium access control protocols

2013· article· en· W2154740911 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIET Communications · 2013
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceAccess controlControl (management)Computer networkArtificial intelligence

Abstract

fetched live from OpenAlex

In the past decade, there has been ever‐increasing research attention to user cooperation in the wireless communication networks. The unique challenges of wireless networks such as channel fading and variation can be addressed well by taking advantage of relaying among cooperating mobile terminals. There are many studies on cooperative communications at the physical layer to exploit spatial diversity for improving channel capacity. In recent years, user cooperation from the perspective of the medium access control (MAC) layer becomes a promising new research area. In this study, the authors present a comprehensive survey on the mainstream cooperative MAC protocols in the literature. Focusing on the contention‐based solutions, the authors classify the well‐known proposals according to how they address two fundamental questions for user cooperation, that is, when to cooperation and whom to cooperate with. In addition to analysing the essential features of classic cooperative MAC protocols, the authors also discuss the major research challenges and project future research directions for MAC‐layer cooperation.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.973
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0080.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.149
GPT teacher head0.392
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