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Record W4245779315 · doi:10.1002/ett.1385

Improved OFDMA uplink transmission <i>via</i> cooperative relaying in the presence of frequency offsets—Part II: Outage information rate analysis

2009· article· en· W4245779315 on OpenAlexaff
Zhongshan Zhang, Chintha Tellambura, Robert Schober

Bibliographic record

VenueEuropean Transactions on Telecommunications · 2009
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversity of British ColumbiaUniversity of Alberta
Fundersnot available
KeywordsTelecommunications linkRelayDiversity gainComputer scienceNode (physics)Computer networkOrthogonal frequency-division multiplexingTransmission (telecommunications)Interference (communication)Electronic engineeringErgodic theoryOrthogonal frequency-division multiple accessFadingTelecommunicationsEngineeringMathematicsPower (physics)Channel (broadcasting)Physics

Abstract

fetched live from OpenAlex

Abstract In Part I of this paper, the ergodic information rate is derived for an orthogonal frequency‐division multiplexing access (OFDMA) uplink with cooperative relaying in the presence of frequency offsets. In this part, the outage information rate of the system is analysed. Both amplify‐and‐forward (AF) and decode‐and‐forward (DF) relays are considered. Each node can play the roles of the source node and the relay, simultaneously, at different subcarriers. Both the outage information rate and the diversity gain are derived for the interference‐limited environment. Numerical results illustrate the superior performance of the proposed cooperative scheme over conventional transmission (without relaying) with regard to outage information rate. Copyright © 2009 John Wiley &amp; 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.

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)0.002
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 score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.256
Teacher spread0.235 · 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

Citations13
Published2009
Admission routes1
Has abstractyes

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