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Record W1728217251 · doi:10.1109/icassp.2001.940512

Wide band channel characterisation in coloured noise using the reversible jump MCMC

2002· article· en· W1728217251 on OpenAlex
D.R. Larocque, J.R. Reilly

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicBayesian Methods and Mixture Models
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMarkov chain Monte CarloReversible-jump Markov chain Monte CarloWidebandComputer scienceChannel (broadcasting)AlgorithmMonte Carlo methodNoise (video)Bayesian probabilityWirelessJumpMarkov chainElectronic engineeringMathematicsTelecommunicationsEngineeringStatisticsArtificial intelligencePhysicsMachine learning

Abstract

fetched live from OpenAlex

This paper presents a novel approach for characterizing wideband (CDMA) multiple dimensional channels for the wireless environment in arbitrarily coloured additive Gaussian noise. This characterization is sufficient for the specification of optimal multichannel space-time receivers. The proposed solution is defined in the Bayesian framework and uses the reversible jump Markov chain Monte Carlo (MCMC) method to obtain estimates of the number of scatterers, their directions of arrival and their times of arrival. The developed method is applied to simulated and real measured data to verify the performance of the approach.

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: Methods · Consensus signal: none
Teacher disagreement score0.969
Threshold uncertainty score0.269

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 science0.0000.000
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 head0.049
GPT teacher head0.258
Teacher spread0.209 · 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

Quick stats

Citations3
Published2002
Admission routes2
Has abstractyes

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