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Record W2054458816 · doi:10.1109/pimrc.2011.6140097

Investigations on the effects of co-channel interference on dual-hop transmission in Nakagami-m fading

2011· article· en· W2054458816 on OpenAlexaff
Salama Ikki, Sonia Aı̈ssa

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCooperative Communication and Network Coding
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité du Québec à Montréal
Fundersnot available
KeywordsNakagami distributionFadingCumulative distribution functionHop (telecommunications)Interference (communication)Probability density functionCo-channel interferenceUpper and lower boundsSignal-to-interference-plus-noise ratioTopology (electrical circuits)Fading distributionChannel (broadcasting)Bounded functionOutage probabilityTransmission (telecommunications)MathematicsSignal-to-noise ratio (imaging)Probability of errorComputer scienceAlgorithmTelecommunicationsStatisticsMathematical analysisPhysicsCombinatoricsRayleigh fading

Abstract

fetched live from OpenAlex

The performance of dual-hop transmission system operating over independent and non-identical Nakagami-m fading channels in the presence of co-channel interference is studied. Exact and upper-bound expressions for the signal-to-interference-plus-noise-ratio (SINR) at the destination are formulated. Then, the cumulative distribution function (CDF) and probability density function (PDF) of the upper bounded SINR are determined. Furthermore, closed-form expressions for the error and outage probabilities are obtained and discussed. Moreover, an approximate PDF of the dual-hop link's instantaneous SINR is derived. Based on said PDF, simple, yet general, asymptotic expressions for the error and outage probabilities are presented and discussed. Numerical and simulation results are provided to verify the tightness of the presented analysis.

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 score0.880
Threshold uncertainty score0.246

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.084
GPT teacher head0.284
Teacher spread0.199 · 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

Citations6
Published2011
Admission routes1
Has abstractyes

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