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Record W2490301240 · doi:10.1109/glocom.1996.587647

Semi-analytical simulation for evaluation of block error rates on fading channels

2002· article· en· W2490301240 on OpenAlexaff
A. Seyoum, Norman C. Beaulieu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsQueen's University
Fundersnot available
KeywordsFadingFading distributionAdditive white Gaussian noiseRayleigh fadingAlgorithmComputer scienceMonte Carlo methodBit error rateStatisticsSignal-to-noise ratio (imaging)Electronic engineeringMathematicsWhite noiseTelecommunicationsDecoding methodsEngineering

Abstract

fetched live from OpenAlex

A semi-analytical simulation method is proposed as a means to improve the efficiency of computer simulation in estimating block error rates in the presence of Doppler fading and additive white Gaussian noise. Frequency non-selective Rayleigh, Ricean and Nakagami fading with diversity combining for binary noncoherent frequency shift keying modulation are considered. The simulation method is based on using the knowledge of the channel and the receiver to determine the conditional probability of bit error analytically; the conditioning is on the correlated fading signal sample energies. In doing so the need to generate noise samples is eliminated and considerable savings in computer simulation time is achieved for high signal-to-noise ratio. Results for the sample size reduction factor compared to the conventional Monte Carlo method are obtained.

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)0.009
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 score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.121
GPT teacher head0.377
Teacher spread0.256 · 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
Published2002
Admission routes1
Has abstractyes

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