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Record W2081466378 · doi:10.1109/tvt.2012.2224679

Regenerative Cooperative Diversity Networks With Co-channel Interference: Performance Analysis and Optimal Energy Allocation

2012· article· en· W2081466378 on OpenAlexaff
Salama Ikki, P. Ubaidulla, Sonia Aı̈ssa

Bibliographic record

VenueIEEE Transactions on Vehicular Technology · 2012
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité du Québec à Montréal
Fundersnot available
KeywordsOutage probabilityCo-channel interferenceComputer scienceInterference (communication)Performance metricMonte Carlo methodFadingChannel (broadcasting)Expression (computer science)Electronic engineeringMathematical optimizationTopology (electrical circuits)Computer networkMathematicsEngineeringElectrical engineeringStatistics

Abstract

fetched live from OpenAlex

A study of the effects of co-channel interference on a multirelay system with decode-and-forward (DF) protocol is presented. Orthogonal relaying is considered, and all relays that correctly decode the message in the broadcasting phase participate in the adaptive relaying phase. First, the effective signal-to-interference-plus-noise ratio (SINR) at the receiver is derived. Then, considering outage as the performance metric, we obtain exact closed-form expression for the outage probability. Simple and general asymptotic expressions for the outage probability, which explicitly show the coding and the diversity gains, are also derived and discussed. Furthermore, we present optimal energy-allocation schemes for minimizing outage under different resource constraints. Monte Carlo simulations are further provided to confirm the analytical results and illustrate the outage performance for different interference conditions and optimization schemes.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.856
Threshold uncertainty score0.705

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.018
GPT teacher head0.235
Teacher spread0.218 · 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 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
Published2012
Admission routes1
Has abstractyes

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