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Record W1530142314 · doi:10.1002/ett.1511

Novel partial decision combining schemes for Rayleigh fading

2011· article· en· W1530142314 on OpenAlex
Yunfei Chen, Norman C. Beaulieu

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

VenueTransactions on Emerging Telecommunications Technologies · 2011
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRayleigh fadingFadingRayleigh scatteringComputer scienceAlgorithmMathematicsElectronic engineeringEngineeringPhysicsDecoding methodsOptics

Abstract

fetched live from OpenAlex

ABSTRACT New combining schemes for partial decisions using one‐bit and two‐bit information of the received samples are derived for Rayleigh fading channels. Compared with previous partial decision combining schemes, these new schemes have either better performances or simpler structures, or both. Simulation results show that some of the new schemes can achieve considerable performance gains over the conventional partial decision combiner in Rayleigh fading or log‐normal shadowed Rayleigh fading, whereas their structures are much simpler than the conventional partial decision combiner. This confirms the advantages of the newly designed combining schemes. Copyright © 2011 John Wiley & Sons, Ltd.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.786
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0030.000
Research integrity0.0000.001
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 head0.088
GPT teacher head0.312
Teacher spread0.224 · 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