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Record W2083185260 · doi:10.1109/glocom.2006.806

WLC33-2: On Relaying in Cooperative Static Channels

2006· article· en· W2083185260 on OpenAlexaff
Reza Nikjah, Norman C. Beaulieu, Masoud Ardakani

Bibliographic record

VenueGlobecom · 2006
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRelayComputer scienceSignal-to-noise ratio (imaging)Channel (broadcasting)Maximal-ratio combiningOutage probabilityPower (physics)Electronic engineeringComputer networkTopology (electrical circuits)TelecommunicationsEngineeringFadingElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

It is shown that regenerative relaying in a dual- hop diversity static or quasi-static relay channel can outperform non-regenerative relaying only if the source-relay link is reliable and power allocation to the source and the relay is optimized. In regenerative relaying, a maximal ratio combining receiver at the destination has an error floor at high signal-to-noise ratios. However, maximal ratio combining is not a maximum-likelihood structure in this application. Maximum-likelihood detection at the destination can remove this error floor, but cannot always make the performance of regenerative relaying surpass that of non- regenerative relaying. A hybrid protocol which avoids some of the limitations of previous relaying schemes is proposed. Conditions under which relaying increases the equivalent source-destination signal-to-noise ratio, and hence, the source-destination channel capacity are derived and the gain of the cooperation is calculated. The analysis provides quantitative measures for choosing the best relay among a set of candidates.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.862
Threshold uncertainty score0.448

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.027
GPT teacher head0.271
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations0
Published2006
Admission routes1
Has abstractyes

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