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Record W2040783219 · doi:10.1109/glocom.2006.854

WLC41-2: Adaptive Regenerate and Forward Cooperative Diversity System based on Quadrature Signaling

2006· article· en· W2040783219 on OpenAlexaff
Veluppillai Mahinthan, J.W. Mark, Xuemin Shen

Bibliographic record

VenueGlobecom · 2006
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsRelayPower consumptionComputer scienceQuadrature (astronomy)Cooperative diversitySignal-to-noise ratio (imaging)Upper and lower boundsChannel (broadcasting)Control theory (sociology)Power (physics)Topology (electrical circuits)Mathematical optimizationElectronic engineeringTelecommunicationsMathematicsEngineeringFadingPhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

In this paper, an adaptive regenerate and forward cooperative diversity (CD) system based on quadrature signaling is proposed. The bit error probability (BEP) of the proposed CD system is derived in terms of the received signal-to-noise ratio (SNR) at the relay and the destination. The derived upper bound of the BEP is validated by simulations. Further, it is shown that the proposed CD system can achieve maximum diversity order of two and performs better even though the inter-user channel is poor. The power consumption of the CD system is location dependent for given BEP. To optimize the power consumption of the proposed CD system, an optimal power allocation strategy is proposed.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.222
Teacher spread0.201 · 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

Citations6
Published2006
Admission routes1
Has abstractyes

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