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Record W1965099004 · doi:10.1049/iet-com.2010.0478

Performance of multi-relay coded cooperative diversity in asynchronous code-division multiple-access over fading channels

2011· article· en· W1965099004 on OpenAlex
Amr Eid, Walaa Hamouda, Iyad Dayoub

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

VenueIET Communications · 2011
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsConcordia University
Fundersnot available
KeywordsComputer scienceFadingCooperative diversityRelayAsynchronous communicationTelecommunications linkConvolutional codeCode division multiple accessComputer networkBase stationBit error rateTelecommunicationsAlgorithmDecoding methodsChannel (broadcasting)

Abstract

fetched live from OpenAlex

In this study, multi-relay decode-and forward (DAF) cooperative networks employing convolutional coding are studied for asynchronous direct-sequence code-division multiple-access (DS-CDMA) systems over frequency-selective slow fading channels. The authors show that the full benefits of coded cooperative diversity cannot be achieved if no multi-user interference suppression is employed at the cooperative end. The authors consider two scenarios; perfect and imperfect inter-user channels. In that, the bit-error-rate performance of the cooperative system is investigated for an uplink transmission where a decorrelator detector is used at both the relay and base station receivers. Both simulation and analytical results are presented to demonstrate the diversity gains of the convolutionally coded cooperative 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.

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: Empirical
Teacher disagreement score0.807
Threshold uncertainty score1.000

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 communication0.0000.001
Open science0.0050.008
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 head0.168
GPT teacher head0.333
Teacher spread0.165 · 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