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Record W2491267448 · doi:10.1109/icupc.1996.557875

On the generation of correlated Rayleigh random variates by inverse discrete Fourier transform

2002· article· en· W2491267448 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProceedings of ICUPC - 5th International Conference on Universal Personal Communications · 2002
Typearticle
Languageen
FieldEngineering
TopicAdvanced Wireless Communication Techniques
Canadian institutionsQueen's University
Fundersnot available
KeywordsDiscrete Fourier transform (general)Computer scienceMultipath propagationAlgorithmRayleigh scatteringRayleigh fadingWirelessFast Fourier transformTransmission (telecommunications)Fourier transformElectronic engineeringFadingMathematicsTelecommunicationsEngineeringFourier analysisFractional Fourier transformOptics

Abstract

fetched live from OpenAlex

Digital computer simulation is widely used to design and develop wireless transmission systems and the components of wireless transmission systems. System performance such as coverage and outage are also frequently assessed by computer simulation. The fading caused by multipath propagation in wireless systems is accurately modeled in some practical cases by the Rayleigh distribution function. This paper presents a modification of the algorithm of Smith (1971) for the generation of correlated Rayleigh random variates by inverse discrete Fourier transform (IDFT) on a digital computer. The new method requires exactly one-half the number of IDFT operations and roughly two-thirds the computer memory of Smith's method. This paper also provides an analysis of the statistical properties of IDFT-based methods and comparisons between the IDFT approach and other approaches to correlated Rayleigh sample generation.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.955
Threshold uncertainty score0.742

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)0.0010.000

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 head0.064
GPT teacher head0.271
Teacher spread0.207 · 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