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Record W2103781515 · doi:10.1109/tcomm.2010.03.070333

UWB receiver designs based on a gaussian-laplacian noise-plus-MAI model

2010· article· en· W2103781515 on OpenAlexaff
Norman C. Beaulieu, S. Niranjayan

Bibliographic record

VenueIEEE Transactions on Communications · 2010
Typearticle
Languageen
FieldEngineering
TopicUltra-Wideband Communications Technology
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAdditive white Gaussian noiseRake receiverElectronic engineeringFadingMultipath propagationInterference (communication)Bandwidth (computing)RakeComputer scienceGaussian noiseMultipath interferenceMatched filterGaussianAlgorithmTelecommunicationsWhite noiseEngineeringPhysicsChannel (broadcasting)Decoding methods

Abstract

fetched live from OpenAlex

A more appropriate statistical model for the multiple access interference than the generally used Gaussian approximation is proposed to reflect the heavy-tailed nature of the multiple access interference in ultra-wide bandwidth systems. Novel receiver structures which surpass the performance of the conventional matched filter receiver are studied for ultrawide bandwidth multiple access communications in both AWGN and fading multipath channels. A proposed Rake receiver is advantageous over the conventional Rake receiver when multiuser interference dominates ambient Gaussian noise and originates from a small to moderate number of interferers. Explanation is provided for the inaccuracy of a Gaussian approximation with regard to the multiple access interference, and the heavy-tailed nature of the probability density function of the multiple access interference is also discussed.

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)0.001
metaresearch head score (Gemma)0.002
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: Methods · Consensus signal: Methods
Teacher disagreement score0.001
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.031
GPT teacher head0.256
Teacher spread0.225 · 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
GenreMethods

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

Citations72
Published2010
Admission routes1
Has abstractyes

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