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Record W2503144029 · doi:10.1109/icc.2016.7510862

AF relaying in underlay spectrum-sharing systems with outdated CSI

2016· article· en· W2503144029 on OpenAlexaff
Jules M. Moualeu, Walaa Hamouda, Fambirai Takawira

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsConcordia University
Fundersnot available
KeywordsUnderlayRayleigh fadingChannel state informationRelayComputer scienceProbability density functionMonte Carlo methodChannel (broadcasting)Cognitive radioCumulative distribution functionTopology (electrical circuits)FadingOutage probabilityUpper and lower boundsTelecommunicationsSignal-to-noise ratio (imaging)Computer networkWirelessMathematicsEngineeringPhysicsStatisticsPower (physics)Electrical engineering

Abstract

fetched live from OpenAlex

In this paper, we study the outage probability of the amplify-and-forward (AF) cognitive relaying in spectrum-sharing environments with outdated channel state information (CSI) over Rayleigh fading channels. A selection of the best relay in the secondary system with outdated channel information is considered. We derive a closed-form expression for the tight lower bound on the end-to-end outage probability using the cumulative density function (CDF) approach. Furthermore, the asymptotic behavior of the outage performance is investigated and reveals the diversity order of the underlying scheme. Finally Monte-Carlo simulations are provided to verify the analytical framework.

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)0.010
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 score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
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 head0.045
GPT teacher head0.260
Teacher spread0.215 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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